EEG wearable devices for continuous monitoring of brain activity and digital neuro-coach using artificial intelligence to map EEG signals into emotional states for enhancing mental wellbeing

Abstract

A wearable electroencephalography device includes an ear cuff around an auricle of an ear comprising a curved structure conforming to contours of the ear, an electroencephalography electrode positioned on the ear cuff and contacting skin for detecting brain activity signals, a wireless controller integrated with the ear cuff and processing the brain activity signals from the electroencephalography electrode, and a wireless communication module transmitting the processed brain activity signals to an external device. The wearable electroencephalography device includes a mobile application receiving the transmitted brain activity signals from the wireless communication module, analyzing the brain activity signals using machine learning algorithms to determine emotional states of a user, and providing real-time feedback regarding the emotional states through a user interface. The mobile application comprises a self-report interface allowing the user to input subjective state data and a live-feed interface displaying real-time subjective state data derived from the brain activity signals.

Description

EEG WEARABLE DEVICES FOR CONTINUOUS MONITORING OF BRAIN ACTIVITY AND DIGITAL NEURO-COACH USING ARTIFICIAL INTELLIGENCE TO MAP EEG SIGNALS INTO EMOTIONAL STATES FOR ENHANCING MENTAL WELLBEINGCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Application Nos. 63652560 and 63653424, filed May 28, 2024 and May 30, 2024, respectively, which are hereby incorporated by reference in their entireties.FIELD

[0002] The present disclosure relates to wearable electroencephalography (EEG) devices for continuous monitoring of brain activity and digital neuro-coaching systems using artificial intelligence, and more particularly to an apparatus comprising an ear cuff wearable device with integrated EEG sensors, a system for mapping EEG signals into emotional states, and methods for enhancing mental wellbeing through real-time neuro-feedback and personalized recommendations. More specifically, the present example implementation pertains to the field of mental wellness devices, specifically to wearable EEG devices for monitoring and recording brain activity. More particularly, the example implementation pertains to an innovative ear cuff designed to provide continuous, non-invasive monitoring of brain activity. This advanced device integrates state-of-the-art EEG sensors and wireless communication capabilities, enabling real-time tracking and analysis of emotional states, stress levels, and overall mental well-being including cognitive function. The ear cuff is engineered to offer comfort, ease of use, and high-fidelity signal acquisition, making it suitable for daily wear and practical for both clinical and personal health applications. Additionally, the present example implementation relates to the field of wearable technology, specifically to a system comprising a wearable electroencephalography (EEG) device and a mobile application designed to monitor and provide feedback on the user's emotional state and mental well-being.BACKGROUND

[0003] The example implementation of a wearable EEG device in the form factor of an ear cuff addresses a significant gap in the market for continuous, non-invasive brain activity monitoring. Electroencephalography (EEG) has been a cornerstone in neuroscience and clinical diagnostics for over a century, allowing scientists and medical professionals to observe brainwave patterns and diagnose neurological conditions. However, traditional EEG systemsare cumbersome, requiring multiple electrodes attached to the scalp and bulky equipment, making them impractical for everyday use by the general population.

[0004] There are different types of brainwaves characterized by different frequencies and the most common ones are Delta waves (less than 4Hz), Theta waves (4-8Hz), Alpha waves (8-12Hz), Beta waves (12-30Hz) and Gamma waves (greater than 30Hz). The Delta waves are usually associated with deep sleep. The Theta waves are associated to REM sleep and states of meditation. The Alpha waves are recorded during wakeftd relaxation with closed eyes, are reduced with open eyes and sleep, while they are enhanced during drowsiness, and have a variant called Mu wave. The Beta waves are associated to waking consciousness and are split into three sections including Low Beta Waves (12.5-16Hz, "Beta 1"); Beta Waves (16.5-20Hz, "Beta 2"); and High Beta Waves (20.5-28Hz, "Beta 3"). The Gamma waves can range up to 140Hz with the 40Hz point being of particular interest, they are correlated with working memory, attention and perceptual grouping.

[0005] In recent years, there has been a growing awareness and emphasis on mental wellbeing and well-being. Chronic stress, anxiety, and other mental wellbeing issues have become increasingly prevalent, affecting millions worldwide. Despite the advances in wearable technology, most devices focus primarily on physical health metrics such as heart rate, steps taken, and calories burned. There is a noticeable lack of devices that provide continuous monitoring and real-time feedback on mental and emotional states.

[0006] High-performers, including tech executives, athletes, and those involved in the quantified-self movement, often face significant physical and mental wellbeing challenges. These individuals experience high levels of stress, anxiety, and pressure to perform, which can lead to issues such as irritability, anxiety, burnout, and physical health problems. While devices like the Apple Watch provide immediate feedback on physical activity and help users track exercise and calorie bum, there is no equivalent for monitoring emotional well-being. This gap forces many to embark on prolonged and arduous journeys towards achieving mental clarity and emotional balance through trial and error in mindfulness practices.

[0007] Traditional methods of monitoring mental wellbeing often involve periodic assessments by healthcare professionals, which can be both time-consuming and expensive. Moreover, these assessments may not capture the day-to-day fluctuations in an individual's emotional state, leading to an incomplete understanding of their mental wellbeing. The development of a wearable EEG device that can be worn comfortably and unobtrusively throughout the day presents a solution to these challenges, offering continuous insights into the wearer's emotional well-being. Previous related art in the field of wearable technology oftenutilized a variety of sensors, including EEG, EMG, fMRI, fNIRS, and others, to provide insights into the user's mental state. However, these devices were typically limited in their form factors, being integrated into items such as hats, headbands, or other headgear like eye glasses, which could be bulky and uncomfortable for extended wear.

[0008] One significant limitation of related art was their lack of seamless integration into everyday accessories, which affected user compliance and continuous use. The form factors were often conspicuous and not aesthetically pleasing, leading to a reluctance in wearing these devices regularly. Additionally, the data collected by these devices, while comprehensive, often required complex interpretation and did not provide real-time feedback that was easily actionable for the user.

[0009] In today's fast-paced world, individuals often experience high levels of stress due to their busy schedules and demanding work environments. The modem lifestyle, characterized by constant connectivity, rapid technological advancements, and the pressure to perform, has significantly increased the mental load on individuals. This persistent state of high demand can lead to chronic stress, which not only affects mental health but also has detrimental impacts on physical well-being, including increased risk for conditions such as hypertension, heart disease, and depression.

[0010] Traditional methods of managing stress and emotions, such as mindfulness practices, cognitive behavioral techniques, and self-reporting, have been widely used. Mindfulness and meditation, for instance, are proven techniques that help individuals cultivate a state of calm and awareness. However, these practices require dedicated time, effort, and consistency, which can be challenging to maintain amidst a hectic schedule. Moreover, the effectiveness of these methods is often subjective, relying heavily on the individual's perception and self-discipline.

[0011] Self-reporting, another common method for managing emotional well-being, involves individuals tracking their moods and stress levels manually. While this approach can provide valuable insights into personal patterns and triggers, it is inherently subjective and prone to bias. Individuals may underreport or overreport their stress levels due to various factors, such as forgetfulness, reluctance to acknowledge certain emotions, or lack of awareness about their true mental state.

[0012] Given these limitations, there is a growing need for a practical, real-time solution that provides objective feedback and helps individuals manage their stress and emotional wellbeing effectively. Advances in wearable technology and machine learning offer promising avenues to address this need. By leveraging sophisticated algorithms and continuous datamonitoring, modem wearable devices can provide accurate, real-time insights into an individual's physiological and emotional states. These devices can track various biomarkers such as heart rate variability (HRV), electrodermal activity (EDA), and brainwave patterns through electroencephalography (EEG).

[0013] The integration of these technologies into a user-friendly application can revolutionize the way stress and emotional well-being are managed. A real-time feedback system that objectively monitors and analyzes physiological data can offer timely interventions and personalized recommendations, helping individuals to better understand and regulate their emotional states. Such a system can prompt users to take breaks, practice breathing exercises, or engage in mindfulness activities precisely when they need it, thus optimizing the efficacy of these interventions.

[0014] Furthermore, an objective, data-driven approach can significantly enhance the self- awareness of individuals. By providing clear, actionable insights into their emotional and physiological states, users can gain a deeper understanding of how various factors in their environment and lifestyle impact their well-being. This awareness can empower them to make informed decisions and adopt healthier habits, leading to improved overall health and quality of life.

[0015] The demand for a practical, real-time solution to manage stress and emotional wellbeing is more pressing than ever. Traditional methods, while beneficial, fall short in the face of modem life's complexities. The advent of advanced wearable technology and machine learning algorithms presents an opportunity to develop innovative solutions that offer objective, continuous monitoring and personalized feedback. Such advancements have the potential to transform stress management, providing individuals with the tools they need to navigate their daily challenges more effectively and maintain their mental and physical health.

[0016] There is a significant gap in the current wearable market when it comes to measuring mental well-being. Most existing wearable devices focus primarily on tracking physical well-being and sleep patterns by monitoring physiological parameters such as heart rate variability (HRV), electrodermal activity (EDA), and skin temperature. These metrics provide insights into the effects of the Autonomic Nervous System (ANS), which plays a crucial role in regulating bodily functions like heart rate and stress responses. While these indicators are valuable for understanding physical health and stress levels, they fall short in providing a comprehensive picture of the user’s emotional and mental states.

[0017] The present example implementation significantly improves upon these limitations by introducing a wearable EEG device in the form factor of an ear cuff. This novel designintegrates advanced EEG sensors within a sleek, comfortable, and aesthetically appealing ear cuff, making it suitable for continuous, all-day wear. The ear cuff form factor is discreet, allowing users to integrate the device seamlessly into their daily lives without drawing attention. The ear cuff form factor of the example implementation is a significant advancement in wearable technology. Unlike headbands or other intrusive devices, an ear cuff is discreet and can be seamlessly integrated into daily life. The design ensures that the device is both comfortable and secure, allowing it to be worn for extended periods without causing discomfort. This is crucial for obtaining accurate and reliable data on brain activity over long durations.

[0018] The improvement in the present example implementation over prior devices lies in its ability to provide continuous, real-time monitoring of brain activity with minimal user intrusion. The advanced EEG sensors embedded in the ear cuff are capable of detecting subtle changes in brainwave patterns associated with different emotional states and stress levels. This real-time data is wirelessly transmitted to a mobile application, where it is processed and translated into actionable insights for the user. The integration of advanced EEG sensors within the ear cuff is a key innovation. These sensors are capable of detecting subtle changes in brainwave patterns associated with different emotional states and stress levels. By continuously monitoring these signals, the device can provide real-time feedback and actionable insights to the user, empowering them to make informed decisions about their mental wellbeing.

[0019] The present example implementation taps directly into the Central Nervous System (CNS) using electroencephalography (EEG) technology, which is considered the gold standard for measuring emotions and mental well-being. Unlike ANS metrics, EEG captures the electrical activity of the brain, offering a direct and nuanced view of the user’s mental state. This advanced technology allows us to monitor and analyze brainwave patterns associated with different emotional and cognitive processes, providing real-time feedback on stress, mood, and overall mental health. By leveraging EEG technology, the example implementation enables users to gain deeper insights into their emotional well-being, helping them to understand and manage their mental health more effectively than ever before.

[0020] One of the most significant challenges in developing such a device is ensuring the accuracy and reliability of the EEG data. The example implementation addresses this by incorporating state-of-the-art signal processing algorithms and noise reduction technologies. These enhancements ensure that the data collected is of high quality, enabling precise interpretation and analysis of the user's brain activity.

[0021] The wearable EEG device is designed to be user-friendly and accessible to a wide range of individuals. It pairs with a mobile application that allows users to log their emotional states, monitor trends, and receive personalized recommendations. This holistic approach to mental wellbeing management ensures that users have the tools and support they need to achieve their wellness goals.

[0022] The potential applications of this technology extend beyond personal use. In clinical settings, the device can be used to monitor patients with neurological conditions, providing continuous data that can aid in diagnosis and treatment. In research, it offers a convenient and non-invasive method for studying brain activity in naturalistic settings, opening new avenues for understanding the human brain and behavior.

[0023] In conclusion, the example implementation of a wearable EEG device in the form factor of an ear cuff represents a significant breakthrough in the field of mental wellbeing technology. By providing continuous, real-time monitoring of emotional states, it addresses a critical need for tools that support mental well-being. This innovation not only enhances the user's understanding of their mental wellbeing but also empowers them to take proactive steps towards achieving emotional balance and resilience.SUMMARY

[0024] This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

[0025] The present example implementation relates to a novel wearable electroencephalography (EEG) device designed in the form factor of an ear cuff. This device represents a significant advancement in the field of mental well-being technology, providing users with a practical and effective solution for continuous monitoring of brain activity and emotional states. The primary objective of this example implementation is to offer a comfortable, aesthetically pleasing, and discreet wearable that can be seamlessly integrated into daily life while delivering high-fidelity EEG data.

[0026] The ear cuff EEG device comprises an ergonomic structure designed to fit securely and comfortably around the auricle of the ear. The device incorporates advanced EEG sensors strategically placed to maintain consistent contact with the skin, ensuring accurate and reliable detection of brainwave patterns. These sensors are capable of capturing a wide range of brainwave frequencies, including Delta, Theta, Alpha, Beta, and Gamma waves, each associated with different mental states and activities.

[0027] One of the key innovations of this example implementation is its ability to provide real-time feedback on the user's emotional states. The EEG data collected by the ear cuff is transmitted wirelessly to a paired mobile application, where sophisticated signal processing algorithms analyze the brainwave patterns. The application translates these patterns into actionable insights, allowing users to understand their current mental state and make informed decisions to manage stress, anxiety, and overall mental well-being.

[0028] The mobile application not only displays real-time data but also features a logging function where users can record their emotional states and daily activities. This information is used to generate personalized recommendations and track trends over time, helping users to identify triggers and patterns in their emotional responses. By providing immediate feedback and long-term tracking, the ear cuff EEG device empowers users to take proactive steps towards improving their mental well-being.

[0029] A significant advantage of the present example implementation is its user-centric design. Unlike traditional EEG devices that are often bulky and uncomfortable, the ear cuff form factor is lightweight, discreet, and stylish. This ensures that users can wear the device throughout the day without discomfort or self-consciousness, thereby increasing compliance and the likelihood of continuous use. The design also allows for easy integration into various lifestyles, whether for professional, athletic, or personal use.

[0030] Additionally, the ear cuff EEG device is equipped with a rechargeable battery that supports extended usage between charges. The device utilizes low-power components and efficient data transmission protocols to maximize battery life, ensuring that users can rely on it for uninterrupted monitoring throughout their day. The integration of noise reduction technologies further enhances the accuracy of the EEG data by minimizing external interferences and artifacts.

[0031] The example implementation also includes a secure and adjustable band that ensures the device remains in place during various activities, from casual daily routines to more vigorous physical exercises. This stability is crucial for maintaining consistent sensor contact and accurate data collection, even in dynamic environments. The device's materials are chosen for their durability, comfort, and hypoallergenic properties, making it suitable for long-term wear.

[0032] The potential applications of the ear cuff EEG device extend beyond personal mental well-being management. In clinical settings, it can be used to monitor patients with neurological conditions, providing continuous data that can aid in diagnosis and treatment. In research, the device offers a convenient and non-invasive method for studying brain activity innaturalistic settings, opening new avenues for understanding human behavior and brain function.

[0033] In summary, the present example implementation provides a groundbreaking solution for continuous, non-invasive monitoring of brain activity through an elegant and practical ear cuff design. By combining advanced EEG sensor technology with user-friendly mobile application features, this device delivers real-time emotional insights, personalized recommendations, and long-term tracking to support users in managing their mental wellbeing. The ear cuff EEG device stands as a testament to the potential of wearable technology to transform mental health management and improve quality of life.

[0034] The present example implementation addresses the need for an advanced system that combines wearable EEG technology with a mobile application to monitor and manage the user's emotional state in real-time. This innovative solution leverages the power of a Digital Neuro-Coach using Artificial Intelligence (Al) to map EEG signals into emotional states, thereby enhancing mental well-being.

[0035] The system pairs a wearable EEG device with a mobile app, providing comprehensive features designed to improve the user's mental health. The wearable EEG device captures brainwave activity and transmits the data to the mobile application, where sophisticated Al algorithms analyze the signals to identify the user's current emotional state. By interpreting these brainwave patterns, the system can provide real-time feedback and actionable insights to help users manage their stress, anxiety, and overall emotional well-being more effectively.

[0036] The Al-driven Digital Neuro-Coach employs various machine learning techniques, such as Support Vector Machines (SVM), Random Forest, and other classifiers, to accurately map EEG signals into specific emotional states. This mapping is based on established models like the Russell's circumplex model of emotions, which categorizes emotions along axes of arousal and valence. By utilizing these advanced Al methods, the system can distinguish between a wide range of emotional states, from calmness and relaxation to heightened alertness and anxiety.

[0037] The mobile app offers a user-friendly interface that allows users to track their emotional states over time, providing visualizations that make complex neural data easy to understand. Features such as mood tracking, personalized recommendations, and a social network component enable users to share their mental health insights with friends and family, fostering a supportive community environment. Additionally, the app includes in-app tutorialsand expert articles to help users make the most of the technology and better understand their mental health data.

[0038] The example implementation provides a groundbreaking solution for continuous, non-invasive monitoring and management of mental well-being. By integrating wearable EEG technology with Al and a comprehensive mobile application, this system empowers users to take proactive steps towards improving their emotional health and resilience in a fast-paced and demanding world.

[0039] The foregoing general description of the illustrative embodiments and the following detailed description thereof are merely exemplary aspects of the teachings of this disclosure and are not restrictive.BRIEF DESCRIPTION OF FIGURES

[0040] Non-limiting and non-exhaustive examples are described with reference to the following figures.

[0041] FIGS. 1-2 illustrate orthogonal views of an EEG electrode assembly with a connecting wire, according to aspects of the present disclosure.

[0042] FIGS. 3-5 illustrate isometric views of a wearable EEG device in an ear cuff form factor, according to aspects of the present disclosure.

[0043] FIGS. 6-8 illustrate isometric views of the ear cuff wearable device of FIGS. 3-5 with a controller housing, according to aspects of the present disclosure.

[0044] FIGS. 9-11 illustrate isometric views of the wearable EEG device of FIGS. 3-5 configured as an ear cuff, according to aspects of the present disclosure.

[0045] FIGS. 12-14 illustrate a block diagram of the EEG wearable device system, according to aspects of the present disclosure.

[0046] FIGS. 15-17 illustrate isometric views of the wearable EEG device of FIGS. 3-5 with an electrode and connecting element, according to aspects of the present disclosure.

[0047] FIGS. 18-20 illustrate isometric views of multiple wearable EEG devices in different ear cuff configurations, according to aspects of the present disclosure.

[0048] FIGS. 21-22 illustrate isometric views of the wearable EEG device of FIGS. 3-5 with a curved extension, according to aspects of the present disclosure.

[0049] FIGS. 23-25 illustrate isometric views of the wearable EEG device of FIGS. 3-5 with a front portion, according to aspects of the present disclosure.

[0050] FIGS. 26-28 illustrate isometric views of multiple ear cuff components in different configurations, according to aspects of the present disclosure.

[0051] FIGS. 29-30 illustrate orthogonal views of a headstrip assembly with a controller and interface cradle, according to aspects of the present disclosure.

[0052] FIG. 31 illustrates a side view of the wearable EEG device of FIGS. 3-5 positioned on a human ear, according to aspects of the present disclosure.

[0053] FIGS. 32-33 illustrate user interface screens for a mobile application with selfreport and live-feed interfaces, according to aspects of the present disclosure.

[0054] FIG. 34 illustrates a mobile application interface displaying a calendar view and data visualization, according to aspects of the present disclosure.DETAILED DESCRIPTION

[0055] The following description sets forth exemplary aspects of the present disclosure. It should be recognized, however, that such description is not intended as a limitation on the scope of the present disclosure. Rather, the description also encompasses combinations and modifications to those exemplary aspects described herein.

[0056] The present example implementation relates to a wearable electroencephalography (EEG) device designed in the form factor of a device applied to the ear. One example implementation comprises three different concepts, each demonstrating unique configurations for the placement of the Bluetooth Low Energy (BLE) controller and the EEG electrode. Figure 1 illustrates these three concepts, highlighting their respective designs and placements.

[0057] Concept A in Figure 2 features the BLE controller positioned in front of the ear tragus, while the EEG electrode is placed behind the ear on the mastoid bone. This configuration ensures that the controller is easily accessible and does not interfere with the natural movement of the ear. The electrode placement on the mastoid bone allows for stable and reliable EEG signal acquisition, taking advantage of the bone's proximity to the brain's electrical activity. Key advantages of Concept A include:• Accessibility of the BLE controller for easy operation and maintenance.• Stable electrode placement on the mastoid bone, providing consistent EEG signal quality.• Minimal intrusion, allowing for comfortable wear throughout the day.

[0058] Concept A of the wearable EEG device features the BLE controller positioned in front of the ear tragus, while the EEG electrode is placed behind the ear on the mastoid bone. To ensure secure and comfortable attachment of the BLE controller and EEG electrode to the skin, various methods can be employed. These methods focus on maintaining stability during daily activities while ensuring ease of use and comfort. Here are some potential methods for attaching the BLE controller to the skin:

[0059] Adhesive Stickers: Medical-Grade Adhesives: Utilizing hypoallergenic, medicalgrade adhesive stickers can securely attach the BLE controller to the skin. These adhesives are designed to be gentle on the skin, preventing irritation and allowing for extended wear. Peel- and-Stick Design: The BLE controller can be equipped with a peel-and-stick backing, making it easy to apply and remove. This method ensures a firm attachment while allowing the user to replace the adhesive as needed.

[0060] Rubber Suction Cups: Small Suction Cups: The BLE controller can be equipped with small rubber suction cups that create a vacuum seal when pressed against the skin. This method provides a secure attachment without the need for adhesives, making it suitable for users with sensitive skin. Reusable Suction Cups: Rubber suction cups can be designed to be reusable and washable, maintaining their suction properties over time. This approach ensures long-term usability and easy maintenance.

[0061] Magnetic Attachment: Magnetic Strips: A thin magnetic strip can be attached to the back of the BLE controller, paired with a corresponding magnetic pad placed on the skin. This method provides a secure and adjustable attachment, allowing for easy repositioning of the controller. Flexible Magnetic Sheets: Flexible magnetic sheets can be used to ensure a comfortable fit against the skin, conforming to the contours of the ear area while maintaining a strong magnetic hold.

[0062] Silicone Straps: Adjustable Silicone Straps: Silicone straps can be designed to wap around the ear, holding the BLE controller in place. These straps can be adjustable to accommodate different ear sizes and shapes, providing a customizable fit. Soft Silicone Loops: Soft silicone loops can be attached to the BLE controller, looping around the ear's tragus and securing the controller in place. This method ensures a comfortable and stable attachment without applying pressure to the skin.

[0063] Clip-On Mechanism: Spring-Loaded Clips: A spring-loaded clip can be integrated into the BLE controller, allowing it to clip onto the tragus or the outer ear. This method provides a secure hold while being easy to attach and detach. Cushioned Clips: Clips with cushioned pads can be used to ensure comfort and prevent pressure points on the skin. The cushioning provides a gentle yet firm attachment, suitable for extended wear.

[0064] Combination Methods: Adhesive and Suction Combo: A combination of adhesive stickers and rubber suction cups can be used to enhance stability. The adhesive ensures initial placement, while the suction cups provide additional hold. Magnetic and Strap Combo: Combining magnetic attachment with silicone straps can provide extra security. The magneticconnection holds the BLE controller in place, while the straps prevent any potential movement during vigorous activities.

[0065] Each of these methods offers unique advantages, catering to different user preferences and skin sensitivities. By exploring various attachment methods, the design can ensure that the BLE controller remains securely in place while providing maximum comfort and ease of use for the wearer. This flexibility in attachment options enhances the overall user experience, making the ear cuff EEG device practical for continuous, all-day wear.

[0066] In Concept B in Figure 3, the BLE controller is integrated directly with the EEG electrode, both positioned behind the ear on the mastoid bone. Different methods are used to attach the EEG electrodes to the skin including but not limited to the methods described for Concept A. This integration reduces the number of separate components and streamlines the design, potentially improving user comfort and device reliability. By combining the controller and electrode into a single unit, Concept B minimizes the physical footprint and simplifies the overall structure.

[0067] Key advantages of Concept B include:• Reduced number of separate components, enhancing user comfort.• Streamlined design, minimizing the device's physical footprint• Integrated functionality, potentially improving reliability and ease of use.

[0068] Concept C in Figure 4 is similar to Concept A but with a variation in the placement of the front component. In Concept C, the BLE controller is connected inside the cymba concha and triangular fossa of the ear, while the EEG electrode remains positioned on the mastoid bone. This configuration offers a more discreet placement for the BLE controller, making it less visible and further integrating the device into the natural contours of the ear. Different methods are used to attach the EEG electrodes to the skin including but not limited to the methods described for Concept A. In another embodiment of the example implementation, the EEG electrode and BLE controller are both placed in the back of the ear and the hook that attaches to cymba concha and triangular fossa of the ear is only used for stability to attach the wearable device to the ear.

[0069] Key advantages of Concept C include:• Discreet placement of the BLE controller, enhancing aesthetic appeal.• Stable electrode placement on the mastoid bone, ensuring consistent EEG signal quality.• Improved integration into the ear's natural contours, providing a seamless and comfortable fit.

[0070] Device Operation: Each concept operates by continuously monitoring the user's brain activity through the EEG electrode. The captured EEG signals are processed by the BLE controller, which wirelessly transmits the data to a paired mobile application. The mobile application analyzes the brainwave patterns, providing real-time feedback and actionable insights to the user. This feedback helps users understand their emotional states, manage stress, and improve their overall mental well-being.

[0071] Technical Specifications:• EEG Electrode: High-sensitivity electrode capable of detecting a wide range of brainwave frequencies, including Delta, Theta, Alpha, Beta, and Gamma waves.• BLE Controller: Low-power Bluetooth module ensuring efficient data transmission and extended battery life. In another embodiment, the controller uses any other type of wireless protocol including but not limited to WiFi, cellular technology (e.g., 4G, 5G, 6G, etc), GPS, satellite.• Power Supply: Rechargeable battery designed for long-duration usage between charges.• Materials: Hypoallergenic and durable materials for the ear cuff, ensuring comfort and suitability for extended wear.

[0072] Applications: The ear cuff EEG device is designed for a wide range of applications, including personal mental well-being management, clinical monitoring of neurological conditions, and research into brain activity and human behavior. Its discreet and comfortable design makes it suitable for continuous, all-day wear, enabling users to seamlessly integrate the device into their daily routines.

[0073] In conclusion, the present example implementation offers a groundbreaking solution for continuous, non-invasive monitoring of brain activity through an elegant and practical ear cuff design. By providing real-time emotional insights and personalized recommendations, this device empowers users to take proactive steps towards improving their mental well-being and overall quality of life.

[0074] Figure 5 shows ear overlay for size and feature approximation. One example implementation, the designs in Figures 1-4 can be adjusted depending on the size and shape of the ear of the individual users. In one exemplary implementation, the size of the hook in Figure 6 depends on the size of the ear and is interchangeable to accommodate different size and shapes of ear.

[0075] Figure 7 discloses Concept D of a wearable EEG device in the form factor of an ear cuff, fitted on a user's ear. The device is shown from both side and rear views, highlighting itsdesign and placement. The ear cuff is designed to wrap around the auricle of the ear, with specific components positioned for optimal EEG signal acquisition and user comfort. The ear cuff is ergonomically designed to fit securely and comfortably around the auricle of the ear. The structure conforms to the natural contours of the ear, ensuring stability and minimizing discomfort during extended wear. The BLE controller is integrated inside the ear cuff or mounted externally to it behind the ear.

[0076] The device comprises a BLE (Bluetooth Low Energy) controller and EEG electrodes. In this figure, the BLE controller appears to be positioned near the top and front part of the ear, around the tragus, while the EEG electrode is located behind the ear on the mastoid bone. This configuration ensures that the controller is easily accessible for operation and maintenance without interfering with the natural movement of the ear.

[0077] The wearable extends with an EEG electrode attached to the mastoid bone via adhesive material or other means like rubber suction cups or similar. The ear cuff is attached using a combination of adhesive materials and possibly other methods such as clips or straps. The adhesive ensures the components remain securely in place during various activities, maintaining consistent contact with the skin for reliable EEG signal acquisition. The materials used for the ear cuff include a combination of hypoallergenic and durable substances to ensure comfort and suitability for extended wear. The visible parts of the device appear to be made of a flexible, possibly metallic material that adapts to the ear's shape, providing a secure and snug fit. The design of the ear cuff is discreet, intended to blend seamlessly with the user's appearance. The compact and unobtrusive form factor allows the device to be worn throughout the day without drawing attention, making it suitable for professional, athletic, or casual use.

[0078] The device continuously monitors the user's brain activity through the EEG electrode. The collected EEG signals are processed by the BLE controller and wirelessly transmitted to a paired mobile application. The application analyzes the brainwave patterns and provides real-time feedback and actionable insights to the user.

[0079] Fi gure 8 illustrates an alternative embodiment of the wearable EEG device designed as an ear cuff. The figure shows the device from both side and rear views, highlighting the specific design elements and placement of the components. The ear cuff is ergonomically designed to fit around the auricle of the ear, with distinct features that enhance its functionality and user comfort. The ear cuff is designed to conform to the natural shape of the ear, ensuring a secure and comfortable fit. The structure wraps around the auricle, providing stability during use.

[0080] In one embodiment, the BLE (Bluetooth Low Energy) controller is positioned on the top and front part of the ear, near the tragus. The EEG electrode is placed behind the ear, on the mastoid bone, which is an optimal location for reliable EEG signal acquisition. The design ensures that both the controller and the electrode maintain consistent contact with the skin for accurate data collection. The ear cuff in this figure employs adhesive materials to attach the components to the skin. The adhesive is visible in the figure, securing the BLE controller near the tragus and the EEG electrode behind the ear. This method provides a stable attachment during various activities, ensuring continuous monitoring without disruption. Additional support is provided by a thin, possibly metallic wire or frame that helps to maintain the shape and position of the ear cuff.

[0081] Figure 9 illustrates another embodiment of the wearable EEG device designed as an ear cuff, demonstrating its specific configuration and placement on a user's ear. The figure provides both side and rear views, showcasing the design elements that contribute to the functionality and comfort of the device. The ear cuff is designed to wrap securely around the auricle of the ear. The structure follows the natural contours of the ear to ensure a stable and comfortable fit. The ear cuff in this figure uses a combination of adhesive materials and possibly additional supports, such as a thin wire or frame, to secure the components to the skin. The adhesive is visible in the figure, securing both the BLE controller near the tragus and the EEG electrode behind the ear. The use of adhesive materials provides a stable attachment, ensuring that the device remains in place during various activities and maintaining consistent sensor contact. The ear cuff is constructed from hypoallergenic and durable materials to ensure comfort and suitability for extended wear. The materials used are designed to minimize irritation and maximize user comfort. The visible parts of the device include flexible and possibly metallic elements that help maintain the shape and position of the ear cuff.

[0082] Figure 10 illustrates another embodiment of the wearable EEG device designed as an ear cuff, showcasing its specific configuration and placement on a user's ear. The figure provides both side and rear views, highlighting the design elements that enhance the device's functionality and comfort. The ear cuff is designed to fit securely and comfortably around the auricle of the ear. The structure conforms to the natural contours of the ear, providing a stable and comfortable fit.

[0083] Figure 11 illustrates two different options of the wearable EEG device designed as an ear cuff, each shown from two different views. The figure provides both side and rear views for each option, highlighting the distinct design elements and placement of the components that enhance the deuce's functionality and comfort. The two options comprise:

[0084] Option 1: in the first two figures, the ear cuff is designed with an external BLW controller and battery module attached to the ear cuff from behind the ear to be less visible. The ear cuff fits securely and comfortably around the auricle of the ear. The structure conforms to the natural contours of the ear, providing a stable and comfortable fit. The ear cuff in this option employs adhesive materials to attach the components to the skin. The adhesive is visible in the figure, securing both the BLE controller near the tragus and the EEG electrode behind the ear. This method provides a stable attachment during various activities, ensuring continuous monitoring without disruption.

[0085] Option 2: the electronics and battery are integrated within the ear cuff. Similar to Option 1, the ear cuff is designed to fit securely and comfortably around the auricle of the ear. The structure conforms to the natural contours of the ear, providing a stable and comfortable fit. The ear cuff in this option also uses adhesive materials to attach the components to the skin. The adhesive is visible in the figure, securing both the BLE controller near the tragus and the EEG electrode behind the ear. This method provides a stable attachment during various activities, ensuring continuous monitoring without disruption.

[0086] Figure 12 illustrates the various design options of the wearable EEG ear cuff, showcasing a range of colors and shapes. This figure highlights the customizable nature of the device, allowing users to select an ear cuff that best suits their personal preferences and style. The ear cuff can detach from the external case so users can have multiple ear cuffs and use them with the same external case interchangeably to have a variety of options to fit their preferred styles.

[0087] Color variant: the ear cuff is available in multiple colors, including but not limited to gold, black, silver, rose gold, translucid and red. These color options provide users with the flexibility to choose a device that matches their aesthetic preferences and style. The different colors are shown as distinct ear cuffs positioned around a model ear, demonstrating the variety available. Material and finish variants include but are not limited to: glossy finish, matte finish, metallic finish.

[0088] Shape variants: the ear cuffs also come in slightly different shapes and finishes. The shapes are designed to fit comfortably around the auricle of the ear while maintaining a stylish appearance. Each shape variant is ergonomically designed to ensure a secure fit, conforming to the natural contours of the ear. Shape variations include but are not limited to• Shape 1: Ergonomic curve following the natural contour of the ear. Sleek and streamlined design for a minimalist look• Shape 2: Slightly wider and more pronounced curve. Bold design with a more noticeable presence on the ear• Shape 3: Compact and rounded design. Subtle and discreet, blending seamlessly with the ear• Shape 4: Elongated and tapered design. Stylish and elegant, adding a touch of sophistication• Shape 5: Angular and geometric design. Modem and edgy, making a statement

[0089] The ear cuff attaches to the ear via one or a plurality of the following methods: adhesive stickers, rubber suction cups, magnetic attachment, silicone straps, clip-on mechanism, combination of adhesive and suction, combination of magnetic and strap, piercing, hinge.

[0090] Figure 13 shows one of the implementations of the ear cuff. This exemplary implementation includes the ear cuff, the external case and the extension for the EEG electrode. The cuff comes in different color (e.g., gold, silver, black, rose gold, red, blue, green, white, pink, purple, grey, bronze, metallic, translucid, matte, glossy), styles (e.g., ergonomic curve, wide curve, compact rounded, elongated tapered, angular geometric, minimalist, bold, subtle, sleek, modem, elegant, edgy, streamlined, polished, understated) and materials (e.g., hypoallergenic silicone, medical-grade adhesive, stainless steel, titanium, ABS plastic, polycarbonate, aluminum, thermoplastic elastomers, gold-plated metal, silver-plated metal, copper, nickel-free alloys, gold, silver, platinum, with or without diamonds). The ear cuff can detach from the external case so users can have multiple ear cuffs and use them with the same external case interchangeably to have a variety of options to fit their preferred styles.

[0091] The external case includes the BLE controller (to transfer EEG data or brainwaves between the device and the smartphone or any other personal device) and the rechargeable battery. The ear cuff is connected to the external case via one or a plurality of pins or with one or a plurality of the following methods: snap-fit, adhesive bonding, magnetic attachment, screw fastening, clip-on mechanism, press-fit, hinge connection, interlocking tabs, sliding mechanism, latch system, velcro straps, hook-and-loop fasteners, elastic bands, wire hooks. In case of magnetic attachment or any other attachment that allows it, the ear cuff can be worn continuously whereas the external case can be removed to be recharged when the battery is low, while the ear cuff is still on the ear.

[0092] In one embodiment, the BLE controller consists of one or a plurality of units disclosed in Figure 14, including but not limited to: System on a Chip (SoC) module (e.g., Nordic, Texas Instruments, ST Microelectronics), analog frontend (AFE), clock generator, status LED, LDO regulator, power button, Li-ion chargeable battery (with different capacity,e.g., 10mAh, 16mAh, 100mAh, etc, and 3.7V), Li-ion charger, USB-C or any other charger connections including, USB charging, contact charging, wireless charging, magnetic charging, solar charging, kinetic charging, battery swapping, contact-based charging, inductive charging, fast charging, trickle charging, cradle / dock charging, Qi standard charging, proprietary charging ports, portable power bank charging, charging via computer interface. The metal dots on the case in Figure 13 are one example of pins used for contact charging. Battery live can ranges from a few hours to over 40 days depending on the battery capacity and optimization of the algorithms.

[0093] The AFE includes but is not limited to one or a plurality of the following components: TSV521, OA4ZHA, TSZ124, MCP6L01, OPA4374, MCP6L02. The AFE comprises one or a plurality of analog filters including low pass, high pass, notch filter to filter out some frequencies such as 50Hz or 60Hz to limit interference from external electronic devices. The SoC operate in one or a plurality of sample rates including 256Hz, 512Hz, etc. and comprises one or a plurality of analog-to-digital converters (ADC) with different bit resolutions including but not limited to 12-bit, 16-bit, 24-bit, etc. In one embodiment, the EEG data and brainwave data is only sent periodically to limit the amount of power consumed by the SoC. In one embodiment, the SoC comprises one or a plurality of wireless or wireline transceivers including but not limited to: Wi-Fi, Bluetooth, NFC, Zigbee, Z-Wave, RFID, Cellular networks (e.g., 2G GSM, 3G WCDMA, HSDPA, 4G LTE, 5G NR, or any 3GPP network). Satellite networks, DSL, Cable modem, Fiber optic, Tl / El, T3ZE3, SONET / SDH, ISDN, Ethernet. The clock generator provides the clock reference to the processor or any other component of the board. In one embodiment, the clock generator is any of the following types of clocks: Atomic clock, Crystal oscillator, Rubidium clock, GPS disciplined clock. Oven- controlled crystal oscillator (OCXO), Temperature compensated crystal oscillator (TCXO), Voltage-controlled crystal oscillator (VCXO), Phase-locked loop (PLL) based clock, MEMS- based clock.

[0094] The extension for the EEG electrode in Figure 13 connects to one or a plurality of EEG electrodes of one or a plurality of types including but not limited to: dry electrodes, wet electrodes, gel electrodes, foam electrodes, gold-plated electrodes, silver / silver chloride (Ag / AgCl) electrodes, stainless steel electrodes, carbon nanotube electrodes, conductive polymer electrodes, textile electrodes (textrodes), disposable electrodes, reusable electrodes, flexible electrodes, rigid electrodes, adhesive electrodes. In one embodiment, the extension connects the BLE controller to the active EEG electrode, whereas the ear cuff is the referenceor ground electrodes. In a different embodiment, the ground EEG electrode is direct connected to the ground of the PCB board inside the case.

[0095] In one embodiment, the EEG electrode are only used to receive brainwaves or brain signals. In another embodiment, the electrodes are antennas or sensors that transmit EMF through the scalp and into the brain, or implement one of a plurality of different types of transcranial stimulation, including but not limited to: transcranial magnetic stimulation (TMS) or transcranial electric stimulation (TES), Transcranial Direct Current Stimulation (tDCS), High-Definition Transcranial Direct Current Stimulation (HD-tDCS), Transcranial Alternating Current Stimulation (tACS), Transcranial Random Noise Stimulation (tRNS), Electroconvulsive Therapy (ECT), Deep Brain Stimulation (DBS).

[0096] In a different embodiment, the device has three separate EEG elctrodes: active, reference and ground.• Active: connected to external electrode: is the primary electrode that captures the electrical activity from a specific area of the scalp. The signal measured is the voltage difference between this electrode and the reference electrode.• Reference: connected to external electrode. Same noise as Active, but without electrical signal. Typically placed on the ear, it is used as a baseline or point of comparison for the active electrode. It is usually placed at a location that is thought to have relatively little or neutral electrical activity, such as the earlobes, mastoid (bone behind the ear), or specific areas on the scalp that are less active. The choice of reference point can significantly affect the EEG data, as it influences what is considered "background" electrical activity.• Ground: connected to ground of board AND to external electrode, for optimal performance and safety, it is recommended to use a dedicated ground electrode in EEG systems, is also used, although it does not contribute directly to the recording of a single EEG channel. The ground electrode serves as a common electrical point for all channels in the system, providing a path for any electrical interference to be safely diverted away from the brain signals being recorded. This helps in reducing noise and improving the overall quality of the EEG data. The ground electrode in an EEG setup can be connected to the ground of the PCB (Printed Circuit Board) of the EEG device, and this is actually a common practice. Connecting the ground electrode to the PCB ground serves several important purposes in EEG recording.

[0097] Figure 15 shows one embodiment of the example implementation where the case is attached to the ear cuff and the EEG electrode via magnetic connections so it’s easier to removethe disposable EEG electrode any time it needs replacement or detach the case from the ear cuff for recharging. It also discloses the displacement of three possible electrodes including but not limited to active, reference and ground, all three used jointly to measure one or a plurality of EEG channels.

[0098] The present example implementations include three or more components including: a hardware platform, a software platform and a network. The hardware platform comprises of a wearable electronic device that has different form factor such as a ear cuff, earrings, necklace, hat, or glasses, or earbuds, or hair clips, or hair band, or headband, or safety helmet, or safety cap, or motorcycle helmet, or racing helmet, or skiing helmet, or climbing hat, or by cycle bump cap, or any other wearable that is worn on someone’s head.

[0099] The software platform comprises an application running on a device or in the cloud, or partially on a device and partially on the cloud. In one embodiment of the example implementations, the application runs on any of the Apple operating systems (OS) including but not limited to macOS, iOS, iPadOS, watchOS, tvOS, HomePod Software, AudioOS, iPod Software. In another embodiment of the example implementations, the application runs on any of the Android operating systems including but not limited to: Android OS, Android Wear OS, Android TV OS, Android Auto OS, Android Things OS, Fire OS, Oxygen OS, One UI, MIUI, EMUI. In another embodiment, the application runs on any type of smartphone or smartwatch devices by any brand included but not limted to: Apple, Samsung, Huawei, Xiaomi, Oppo, Vivo, OnePlus, Google, LG, Sony, HTC, Motorola, Nokia, Asus, Lenovo, ZTE, Meizu, BlackBerry, Alcatel, TCL. In one embodiment, the application is running on one or a plurality of smartphone devices, including but not limited to: Apple iPhone 13 series (iPhone 13, iPhone 13 mini, iPhone 13 Pro, iPhone 13 Pro Max), Samsung Galaxy S21 series (Galaxy S21, Galaxy S21+, Galaxy S21 Ultra), Google Pixel 6 and Pixel 6 Pro, Xiaomi Mi 11 series (Mi 11, Mi 11 Pro, Mi ll Ultra), OnePlus 9 and OnePlus 9 Pro, Oppo Find X3 series (Find X3 Pro, Find X3 Neo, Find X3 Lite), Vivo X60 series (X60, X60 Pro, X60 Pro+), Motorola Edge 20 series (Edge 20, Edge 20 Pro, Edge 20 Lite), Nokia X20 and X10, Sony Xperia 1 III and Xperia 5 III. In one embodiment, the application is running on one or a plurality of smartwatch devices, including but not limited to: Apple Watch Series 1-7, Samsung Galaxy Watch 4 and Watch 4 Classic, Fitbit Versa 1 -3, Sense and Sense 2, Garmin Venu 2 and Venu 2S, Fossil Gen 5E and Gen 6, TicWatch Pro 3, Amazfit GTS 2 and GTR 2, Huawei Watch GT 2 Pro, Oppo Watch 2.

[0100] In another embodiment of the example implementations, the application runs on any type of cloud, including but not limited to: far-edge cloud, edge-cloud, Public cloud, Private cloud, Hybrid cloud, Community cloud, Distributed cloud, Multi-cloud, Inter-cloud,Fog / cloud edge, Serverless cloud. In another embodiment the application runs on any cloud by different companies including but not limited to: Amazon Web Services (AWS) - Amazon Elastic Compute Cloud (EC2), Amazon Simple Storage Service (S3), Amazon Relational Database Service (RDS), Microsoft Azure - Azure Virtual Machines, Azure Blob Storage, Azure SQL Database, Google Cloud Platform (GCP) - Compute Engine, Cloud Storage, Cloud SQL, IBM Cloud - Virtual Servers, Object Storage, Databases for MongoDB, Oracle Cloud - Compute, Storage, Database, Alibaba Cloud - Elastic Compute Service (ECS), Object Storage Service (OSS), Relational Database Service (RDS).

[0101] The network is one or a plurality of wireless networks including but not limited to:Wi-Fi, Bluetooth, NFC, Zigbee, Z-Wave, RFID, Cellular networks (e.g., 2G GSM, 3G WCDMA, HSDPA, 4G LTE, 5G NR, or any 3GPP network), Satellite networks. In another embodiment of the example implementations, the network is one or a plurality of wireline networks including but not limited to: DSL, Cable modem, Fiber optic, Tl / El, T3 / E3, SONET / SDH, ISDN, Ethernet.

[0102] Figure 16 is a variation where the case is removed from the cuff and attached to two separate adhesive EEG electrodes to be applied directly over the scalp or through hair to avoid using the ear mount. The same figure also shows the displacement of the active, reference and ground electrodes.

[0103] Figure 17 shows one exemplary charger station for the device.

[0104] Fi gure 18 shows another variation of the EEG device from different points of views.

[0105] Figure 19 shows a different embodiment where there is no extension for the EEG electrode which is instead directly attached to the case. In one embodiment, the EEG electrode is removable and disposable. In another embodiment, the EEG electrode is retrofitted with a spring-loaded system to push the electrode against the mastoid bone or the scalp to make good contact to the skin to improve the quality of the EEG signal. In another embodiment of the example implementation, the two silver dots on the case are the reference and the ground EEG electrodes respectively, whereas the third electrode coming out of the back is the active electrode. In another embodiment, the three electrode are any combinations of active, reference and ground electrodes. In another embodiment, the ear cuff itself is the reference or ground electrode. Figure 20 shows a variation of the system in Figure 19 where the ear cuff is replaced with one of a different style.

[0106] Figure 21 shows a different EEG wearable that wraps around the ear. The case contains the controller, electronics, the battery and one EEG electrode that makes contact with the skin. The opposite end of the support that wraps around the ear is the EEG electrode. Inone embodiment, the active EEG electrode is mounted directly on the surface of the case touching the front part of the ear, whereas the reference electrode is behind the ear at the opposite end of the hook for the ear support, and the ground electrode is directly grounded to the ground of the PCB board inside the case. In a different embodiment, the three electrodes are placed at any combinations of the locations indicated in Figure 21 and above.

[0107] Figure 22 compares all four different types of EEG wearables described above.

[0108] Figure 23 shows yet another embodiment of the example implementation with a different design of the EEG wearable. In one embodiment, the ear cuff acts like the reference electrode and clips magnetically to the ear. The active electrode extends from the ear cuff and end with an electrode filled with soft material to ensure that the EEG electrode makes good contact with the skin to guarantee good EEG signal. The extension is designed to push the electrode against the skin via e.g., spring-loaded system. The soft material include but is not limited to: Silicone gel, memory foam, medical-grade silicone, thermoplastic elastomers (TPE), polyurethane foam, conductive foam, hydrogel, soft rubber, viscoelastic foam, elastomeric gel, soft silicone rubber, gel pads, silicone rubber foam, conductive silicone, soft polymer gel.

[0109] Figure 24 shows yet a different embodiment of the example implementation where the ear cuff comprises a hinge to simplify the application of the cuff to the ear. In one embodiment the hinge is one or a plurality of hinges such as: spring hinges, barrel hinges, butt hinges, piano hinges, pivot hinges, continuous hinges, concealed hinges, friction hinges, torque hinges, butterfly hinges, leaf hinges, invisible hinges, soft-close hinges, snap hinges, detent hinges. In another embodiment, the active EEG electrode is the bump extending out of the case and the reference electrode is the ear cuff itself.

[0110] Figure 25 discloses a different embodiment of the example implementation where the active electrode is placed directly on the side of the case as at the top left quadrant of the figure. The hinge design is of different types with single or multiple attachments to the case. The ear cuff is designed in different colors including but not limited to silver, gold or black.

[0111] Figure 26 shows different types and views of the EEG wearable with different colors, shapes and styles of the cuff. In all embodiments, the ear cuff can detach from the external case so users can have multiple ear cuffs and use them with the same external case interchangeably to have a variety of options to fit their preferred styles.

[0112] Figure 27 shows one exemplary of the example implementation where the ear cuff of different color and style is removed from the case for mounting a different cuff.

[0113] Figure 28 shows different styles and color of the ear cuff. The case shows three metal dots for contact charging, one silver EEG electrode at the main surface of the case as the reference electrode and one EEG electrode on the side of the case as the active electrode. In one embodiment, the ground electrode is directly connected to the ground of the PCB and electronic circuitry inside the case.

[0114] In one embodiment, the active and reference electrodes measure the EEG signal and brainwave in the temporal part of the skull, including but not limited to EEG electrode locations A1, T3, T5, A2, T4, T6 of the 10-20 system or LPA, T7, FT7, FT9, TP7, TP9, RPA, T8, FT8, FT10, TP8, TP10 of the 10-10 system.

[0115] Key Advantages:• Secure and Comfortable Fit: The ergonomic design and secure attachment methods ensure the device remains in place and comfortable throughout the day.• Accurate EEG Signal Acquisition: The strategic placement of the EEG electrode on the mastoid bone provides stable and reliable detection of brainwave patterns.• User-Friendly Design: The discreet and aesthetically pleasing design increases user compliance and the likelihood of continuous use.• Versatile Applications: The device is suitable for various applications, including personal mental well-being management, clinical monitoring, and research into brain activity and human behavior.

[0116] In summary, here are some of the features of the example implementation:• Ergonomic Design: Fits securely around the ear and uses advanced EEG sensors to maintain consistent skin contact for accurate brainwave detection.• Real-Time Feedback: Collects EEG data and transmits it wirelessly to a mobile app that provides actionable insights.• Mobile Application: Displays real-time data, logs emotional states and activities, and offers personalized recommendations based on trends.• User-Centric Design: Lightweight, discreet, and stylish, ensuring comfort and increasing user compliance.• Battery and Components: Features a rechargeable battery, low-power components, and noise reduction technologies.• Secure Attachment: Includes an adjustable band for stability during various activities and uses hypoallergenic materials.• Applications: Suitable for personal mental well-being, clinical monitoring of neurological conditions, and research.

[0117] Figure 29 shows a different embodiment of the example implementation comprising of a headstrip that can be attached directly to the head or to any headwear such as caps, hat or headbands, or any other headwear. In one embodiment of the example implementation, the front of the headstrip comprises one or a plurality of EEG electrodes or different shapes including but not limited to circular, rectangular, square, oval, triangular, hexagonal, elliptical, star-shaped, teardrop, crescent, diamond, trapezoidal, semi-circular, dome-shaped, concave, convex. The EEG electrode one or a plurality of different types including: Dry electrodes, wet electrodes, gel electrodes, foam electrodes, gold-plated electrodes, silver / silver chloride (Ag / AgCl) electrodes, stainless steel electrodes, carbon nanotube electrodes, conductive polymer electrodes, textile electrodes, comb electrodes, microelectrodes, flexible electrodes, rigid electrodes, adhesive electrodes, sponge electrodes, disposable electrodes, reusable electrodes, needle electrodes, capacitive electrodes.

[0118] The back side of the headstrip comprises one or a plurality of sticky materials including but not limited to: medical-grade adhesive, silicone adhesive, double-sided tape, hydrogel adhesive, conductive adhesive, pressure-sensitive adhesive, acrylic adhesive, epoxy adhesive, polyurethane adhesive, butyl rubber adhesive, thermoplastic adhesive, hot melt adhesive, adhesive gel pads, fabric adhesive, velcro strips. In one embodiment, the front makes contact with the forehead whereas the back is attached to any headwear including but not limited to caps, hats, headbands, etc. The BLE controller or pod is attached to the center of the headstrip. In a different embodiment, the adhesive material is on the front side of the headstrip to attach it directly to the forehead without any support of any type of headwear. In one embodiment, the EEG electrodes are positioned at any location of the 10-20 system including but not limited to: Fp1, Fp2, F7, F3, Fz, F4, F8. In a different embodiment, the electrode locations are from the 10-10 system including but not limited to: AF7, Fp1, Fpz, Fp2, AF8, AF3, AFz, AF4.

[0119] Figure 30 shows a different embodiment of the example implementation where the BLE controller is attached to the EEG electrodes and fits inside the interface cradle with adhesive material attached to the opposite side. The functionality of the cradle is to stick to specific headwear while the controller can be removed. For example if someone has five different hats, then five different interface cradles would stick to the five hats permanently through the adhesive material, whereas the controller can be removed and attached dynamically to different hats. In other words, only one controller can be dynamically attached to mulitple hats.

[0120] In a different embodiment, the EEG wearable device in Figure 21 is wrapped around the ear like in Figure 31 to guarantee good contact with the ear for good EEG signal while providing an esthetically appealing look. In one embodiment, the EEG device is designed like a jewel to be appealing and portable.

[0121] In a different embodiment, the wearable device comprises of one or a plurality of sensors including but not limited to photoplethysmography (PPG), LED with green, red on infrared, or Electrodermal Activity (EDA), functional near-infrared spectroscopy (fNIRS) sensors, accelerometer, or gyro sensors, Magnetoencephalography (MEG), positron emission tomography (PET), event-related optical signal (EROS), electrocardiogram (ECG or EKG), or 3-axis gyroscope, or accelerometer, or global positioning system (GPS) receiver, or barometer, or body or skin temperature, or proximity sensor, or ambient light sensor, or any ID sensors. One embodiment uses any type of antennas (e.g., dipoles, patch antennas, microstrip antennas, ferrite rod antennas or any radio frequency (RF) antenna used in wireless communications, ferrite material) that receive or transmit electromagnetic fields (EMF), since brainwaves are indeed electromagnetic waves generated by charged particles as a result of neural activities in the brain and as such they are modeled by the same Maxwell equations as EMF.

[0122] In one embodiment of the example implementations the sensors are EDA sensors integrated to the ear cuff wearable that measure the galvanic skin response. For example, the EDA sensor is used to measure the phasic skin conductance response (SCR) and the tonic skin conductance level (SCL) at the mastoid bone to compute biological parameters that indicate the level of arousal of the person wearing the ear cuff. In another embodiment, this arousal level from the EDA sensor is combined with the information about brain signals from EEG electrodes to determine different levels of arousal and valence characterizing the emotional states of the individual wearing the hat. In a different embodiment, the sensors are PPG sensors used to compute the heart rate (HR), resting heart rate (RHR), Heart rate variability (HRV), Blood oxygen saturation (SpO2), Respiratory rate, Blood pressure, Cardiac output, Stroke volume, Arterial stiffness, or Vascular resistance, interbeat interval (IBI). In another embodiment, different combinations or EEG, EDA, PPG, or skin or body temperature sensors are installed on the ear cuff wearable or any other location throughout the ear cuff or case to measure brainwaves, brain signals and different biological parameters for detecting and classifying emotional states of the person wearing the ear cuff, including but not limited to levels of arousal or levels of valence.

[0123] The ear cuff EEG device features an on-off button that allows users to power the device on and off with ease and an LED light that serves multiple functions. The LED lightand on-off button on the ear cuff EEG device are designed to provide a wide range of functionalities that enhance user experience and device usability. The LED light communicates important information through visual indicators, while the on-off button offers control over various device operations, ensuring a versatile and user-friendly interaction.

[0124] Uses of the LED light include but are not restricted to: Bluetooth Pairing Status and successful connection, battery notifications, charging status, activity alerts notifications to users, mode indications for different operational modes of the device such as stand by, sleep, active, etc; error alerts to indicate device errors or malfunctions.

[0125] Uses of the on-off button include but are not restricted to: turning the device on and off; bluetooth pairing activation; mode switching to switch between operational modes; resetting the device; activating specific functions such as starting or stopping EEG recording or triggering a particular type of monitoring.

[0126] System overview: the example implementations discloses a wearable EEG device that pairs seamlessly with a mobile application. This system acts as a companion, providing real-time neuro-feedback and actionable insights into the user's emotional state. The current example implementation improves upon the previous platform by addressing these limitations with a more focused and user-centric approach to emotional and mental health management:

[0127] Focused Application: The current example implementation specifically targets the monitoring and management of emotional states and stress levels, providing a more focused and effective solution for mental wellness compared to the general-purpose nature of the previous platform.

[0128] Real-Time Neuro-Feedback: By integrating a wearable EEG device with a mobile app, the current example implementation provides real-time neuro-feedback, allowing users to receive immediate insights into their emotional states and stress levels. This enhances user engagement and offers actionable feedback that was lacking in the previous platform.

[0129] Companion and Self-Report Features: The mobile app acts as a companion, nudging users to take breaks and breathe during intense workdays. The Self-report tab helps users track their emotions throughout the day, building self-awareness and emotional resilience. These features offer a personalized and proactive approach to mental wellness management.

[0130] Objective and Subjective Monitoring: The Live tab provides objective neuro- feedback using EEG signals and machine learning classifiers to map EEG data into emotional states with high accuracy and resolution. The Journal tab shows stress peaks and valleys, helping users identify patterns and make informed decisions for better stress management.

[0131] Validation and Emotional Support: The system validates users' experiences during stressful periods, providing emotional support and encouraging positive habits, such as taking walks during lunchtime. This focus on user validation and support was not present in the related art.

[0132] Integration with User's Lifestyle: The current example implementation helps users be present and improve their emotional well-being, showing up better for themselves and their loved ones. It integrates seamlessly into users' daily routines, enhancing the practicality and effectiveness of the solution.

[0133] The current example implementation offers a significant advancement over the previous platform by providing a dedicated, user-focused solution for emotional and mental health management. It combines real-time neuro-feedback, personalized insights, and proactive support to help users manage their stress and emotional well-being effectively. The application comprises of multiple features including:

[0134] Demographic and lifestyle data collection: the app enables users to record demographic and lifestyle data to be used to develop personalized recommendations using machine learning models. The data captures includes but it is not limited to: gender, year of birth, dominant hand, physical activity level, education, language ability, musical ability and regular wellness practice.

[0135] Navigation bar: includes one or a plurality of the following features - Self-report tab: the current page is indicated by the highlighted Self Report icon (yellow) on the navigation bar at the bottom of the screen. This helps users easily identify which feature they are using. Live feed tab: provides access to real-time neuro-feedback based on EEG signals. Journal tab: allows users to view historical data and trends in their emotional states, offering insights into patterns and triggers.

[0136] Self-report page: the Self-report tab allows users to subjectively track their emotions throughout the day. By recording their feelings, users can build self-awareness and emotional resilience. This feature provides a personal journal of emotional states, helping users reflect on their emotional well-being. The Self-report page presents a user-friendly interface designed to facilitate the tracking and reporting of emotional states throughout the day. This page integrates several novel features that enhance user engagement and accuracy in self-reporting emotional well-being.

[0137] Greeting and query: The page greets the user with a personalized welcome message: "Welcome back, [User's Name]," followed by a prompt asking, "How are you feeling?" Thispersonalization enhances user engagement and creates a welcoming atmosphere for selfreporting.

[0138] Emotional state slider - comprises of a plurality of features including but not limited to: Interactive slider: at the center of the page, there is a semi-circular slider that allows users to indicate their current emotional state. The slider ranges from "very calm" (blue) on the left to "very alert" (orange) on the right, with a neutral point in the center. Color-coded scale: the color gradient from blue to orange visually represents the emotional spectrum, making it intuitive for users to select their current state. The use of colors helps in quick recognition and response. Draggable indicator: a draggable indicator (circular button) is placed on the slider, allowing users to easily select their precise emotional state by dragging the button along the scale. By interacting with the sliders, users should be able to see how the emotion labels on top of the sliders change value accordingly.

[0139] Emotional state submission - comprises of a plurality of features including but not limited to: Pre-defined text feedback: below the slider, there is a text box that dynamically updates to reflect the selected emotional state, e.g., "I feel neutral." This text provides immediate feedback to the user about their selection. Submit button: a prominently displayed "Submit" button allows users to record their selected emotional state. This action stores the data for future analysis and feedback.

[0140] Some of the novelties of the self-report page are the following:1. Dynamic Color Gradient Scale:The use of a dynamic, color-coded gradient scale for emotional states is innovative, providing an intuitive and visually engaging way for users to report their feelings. This approach simplifies the reporting process and enhances user interaction.2. Real-Time Text Feedback:The integration of real-time text feedback corresponding to the selected emotional state is novel. It offers immediate confirmation to the user, ensuring that their input is accurately captured and understood.3. Personalized User Experience:The personalized greeting and context-sensitive query ("How are you feeling?") create a more engaging and user-centric experience, encouraging regular use and accurate self-reporting.4. Seamless Navigation:The easy-to-use navigation bar at the bottom of the screen allows users to quickly switch between different features of the app, enhancing the overall usability and accessibility of the application.5. Data Integration for Emotional Tracking:By allowing users to submit their emotional states throughout the day, the app collects valuable data that can be integrated into personalized feedback and insights, fostering better self- awareness and emotional resilience.

[0141] The Self-report page provides a sophisticated yet user-friendly interface for tracking emotional states. Its innovative features, including the dynamic color gradient scale, real-time text feedback, personalized user experience, seamless navigation, and data integration, collectively enhance the effectiveness of emotional self-reporting and support the overall goal of improving mental well-being.

[0142] Live-feed page: provides objective neuro-feedback about the user's emotions. The wearable EEG device measures EEG signals and uses machine learning classifiers to map these signals into emotional states in real-time, with a resolution of one second or less. This feature offers immediate, accurate feedback on the user's emotional condition. The mobile app serves as a companion, nudging the user during intense workdays filled with meetings and reminding them to take a moment to breathe and recover. This feature helps the user manage stress by incorporating short breaks into their routine. The Live-feed page of the example implementation app provides users with real-time neuro-feedback about their emotional states. This page integrates several innovative features that enhance the user experience by offering immediate and actionable insights based on EEG data.

[0143] Connection Indicator aa green, red or yellow dot labeled "Connected / Disconnected / Connecting" indicates the status of the connection between the wearable device and the mobile app. This visual indicator reassures users that the device is actively monitoring and transmitting data.

[0144] Real-Time EEG Signal: Below the connection status, a horizontal graph displays the real-time EEG signal waveform. This graph provides users with a visual representation of their brainwave activity as it is being recorded.

[0145] Interactive Slider: Similar to the Self-report page, this page includes a semi-circular slider that shows the user's current emotional state. The slider ranges from "very calm" (blue) on the left to "very alert" (orange) on the right, with the current state indicated by a black dot on the scale. Dynamic Text Feedback: The text above the slider dynamically updates to reflect the real-time emotional state of the user, such as "slightly alert." This immediate feedback helps users understand their current mental state at a glance.

[0146] Color-Coded History Graph: Below the emotional state slider, a detailed history graph shows the fluctuations in the user's emotional state over time. The graph uses a colorgradient from blue (very calm) to orange (very alert) to visually represent changes in emotional state.

[0147] Time Scale: The x-axis of the graph is labeled with time intervals, showing the progression from "15 minutes ago" to "now." This allows users to track how their emotional state has changed over the recent past. This graph can also be displayed vertically, with time represented on the y-axis and arousal on the x-axis. This provides a novel representation of emotional states, eliminating the bias associated with traditional top-down approaches.

[0148] Some of the novelties of the Live-Feed Page:1. Real-Time Neuro-Feedback:• The integration of real-time EEG signal monitoring and immediate feedback on the user's emotional state is a significant innovation. This feature allows users to receive instant insights into their mental state, enabling timely interventions to manage stress and emotions.2. Dynamic Emotional State Indicator:• The combination of a color-coded slider and dynamic text feedback provides an intuitive and engaging way for users to understand their current emotional state. This visual and textual representation enhances user comprehension and interaction.3. Historical Data Visualization:• The inclusion of a detailed history graph that tracks emotional state fluctuations over time is novel. This feature helps users identify patterns and trends in their emotional responses, providing valuable insights for self-awareness and emotional regulation.4. User-Friendly Interface:• The seamless integration of real-time data visualization, dynamic feedback, and historical trends in a single page creates a user-friendly interface. This comprehensive approach ensures that users can easily access and interpret their emotional state data.5. Enhanced Engagement:• By offering continuous monitoring and feedback, the Live-feed page encourages users to engage regularly with the app. This ongoing interaction supports the app's goal of improving mental well-being through increased self-awareness and emotional resilience.

[0149] Journal data: displays peaks and valleys of the user's stress levels throughout the day. This visualization helps users identify patterns and make informed decisions to improve their well-being, such as incorporating walks during lunchtime to reduce stress. The system validates the user's feelings during challenging periods by highlighting instances of high daytime stress. This feature reassures users that their experiences are recognized and normal, providing emotional support. The Journal page of the example implementation app providesusers with a comprehensive overview of their emotional states over a specified period, allowing them to track and analyze their mental well-being. This page integrates several innovative features designed to enhance user engagement and provide actionable insights.

[0150] Calendar Display: At the top of the page, a monthly calendar is displayed, showing the days of the current month. Each day is color-coded based on the user's dominant emotional state for that day.

[0151] Any combination of color coding is used including but not limited to: Blue Shades: Represent calm states. Different shades of blue indicate varying levels of calmness. Yellow Shades: Represent alert states. Different shades of yellow indicate varying levels of alertness. White: Indicates neutral states. Navigation Arrows: Arrows on either side of the month allow users to navigate to previous or upcoming months, providing easy access to historical data.

[0152] Selected Day Highlight: The currently selected day is highlighted with a white circle, providing a clear indication of the day being analyzed. Emotional State Summary: Directly below the calendar, a summary of the selected day's emotional states is displayed. For example, "43 min Calm" and "31 min Alert" indicates the total duration of each state throughout the day.

[0153] Detailed Time Graph: 24-Hour Graph: A detailed graph below the daily summary shows the fluctuations in the user's emotional states throughout the selected day. Time Scale: The x-axis of the graph is labeled with time intervals, ranging from midnight to midnight, providing a full 24-hour view. Color-Coded Emotional States: The graph uses the same colorcoding scheme (e.g., blue for calm, yellow for alert) to visually represent changes in emotional state over time. This visual representation helps users identify patterns and triggers for their emotional states. Detailed Insights: Users can observe specific times of day when they experienced significant calmness or alertness, allowing for a deeper understanding of their emotional rhythms.

[0154] Some of the novelties of the Journal Page:1. Color-Coded Calendar Overview:• The use of a color-coded calendar to represent daily emotional states is a novel feature. This visual summary allows users to quickly identify patterns and trends in their emotional well-being over an extended period.2. Detailed Daily Summaries:• The inclusion of daily summaries that break down the total duration of calm and alert states provides users with a clear understanding of their emotional balance each day.3. Comprehensive Time Graph:• The 24-hour graph offers a detailed and granular view of emotional state fluctuations, helping users pinpoint specific times of day that may be more stressful or calming. This level of detail supports more targeted interventions and self-awareness.4. Historical Data Navigation:• The ability to navigate through previous months allows users to track long-term trends and changes in their emotional states, supporting a holistic approach to mental well-being management.5. User-Friendly Interface:The seamless integration of calendar, daily summaries, and detailed graphs into a single page creates a user-friendly interface. This comprehensive approach ensures that users can easily access and interpret their emotional state data.6. Enhanced Engagement:• By providing both high-level summaries and detailed insights, the Journal page encourages regular engagement with the app. This ongoing interaction supports the app's goal of improving mental well-being through increased self-awareness and emotional resilience.

[0155] The application pairs with a wearable device is designed for comfort and continuous use. It captures high-fidelity EEG data and transmits it to the mobile app for real-time analysis. The device is equipped with advanced sensors and a rechargeable battery, ensuring long-term usability and accuracy.

[0156] Advantages: the primary objective of the app is to help users be present and show up for themselves and their loved ones by managing their mental well-being effectively. The combination of subjective self-reporting and objective neuro-feedback provides a comprehensive approach to emotional health, empowering users to take proactive steps in managing stress and enhancing their quality of life.

[0157] The present example implementation includes a novel social network feature integrated into the wearable EEG device and its companion mobile application. This feature is designed to create a supportive community of friends and family members who can share and monitor each other's brainwave data. By leveraging advanced brainwave monitoring technology, this feature aims to foster a connected environment where emotional well-being is prioritized, and timely support is provided to individuals experiencing prolonged periods of distress.

[0158] The social network feature enables users to invite friends and family members to join their monitoring circle. Once connected, members of this circle can share their brainwave features, such as levels of arousal, anxiety, and overall mental state. The system continuouslyanalyzes the EEG data collected by the wearable device, looking for patterns indicative of high arousal or anxiety that persist for extended periods. When the app detects that a user has been experiencing heightened anxiety or arousal for several consecutive days, it triggers an alert to notify the user's closest friends or family members.

[0159] This alert system is a proactive measure designed to prevent the escalation of anxiety into more severe mental health issues, such as depression. By notifying trusted individuals in the user's social network, the app encourages them to reach out and check on the user's well-being. This feature not only promotes emotional support and intervention but also helps to build a sense of community and mutual care among users. The timely notifications enable friends and family to provide emotional support, suggest coping mechanisms, or encourage seeking professional help if necessary.

[0160] Furthermore, the social network feature includes privacy controls, allowing users to customize what information is shared and with whom. Users can choose to share only specific metrics or their overall mental state, ensuring that their privacy is respected while still benefiting from the community's support. This feature represents a significant advancement in integrating social support with personal health monitoring, harnessing the power of community to enhance mental wellness and resilience.

[0161] The integration of a social network feature into the wearable EEG device and its mobile application provides a comprehensive approach to mental health management. By enabling users to share their brainwave data with trusted individuals and alerting their network during times of prolonged distress, this feature fosters a supportive environment that can help mitigate the effects of anxiety and prevent the onset of depression. This innovation underscores the potential of combining technology with social support to improve overall emotional wellbeing.

[0162] The present example implementation not only provides a social network feature for monitoring anxiety and high arousal but also extends its capabilities to measure and track a wide range of emotions using the Russell's circumplex model of affect. This model is a widely recognized framework in psychology that categorizes emotions along two dimensions: arousal (ranging from high to low) and valence (ranging from positive to negative). By utilizing this model, the application can offer a comprehensive analysis of an individual's emotional state, covering emotions such as excitement, contentment, boredom, and frustration, among others.

[0163] To achieve this, the app employs advanced machine learning (ML) classifiers to interpret the EEG signals captured by the wearable device. The system uses a variety of algorithms, including Support Vector Machines (SVM), Random Forests, and other state-of-the-art machine learning techniques. These classifiers are trained on extensive datasets of EEG signals associated with different emotional states, enabling the app to accurately map real-time brainwave data to specific emotions.

[0164] The process begins with the continuous monitoring of EEG signals, which are then preprocessed to filter out noise and enhance signal quality. The refined signals are fed into the machine learning classifiers, which analyze the patterns and frequencies of the brainwaves. S VM classifiers, known for their robustness in high-dimensional spaces, are used to distinguish between different emotional states by finding the optimal hyperplane that separates the EEG data points. Random Forest classifiers, on the other hand, leverage the power of multiple decision trees to improve the accuracy and reliability of emotion detection by considering various aspects of the EEG data.

[0165] In addition to SVM and Random Forest, the app integrates other machine learning algorithms to enhance its predictive capabilities. These algorithms include Neural Networks for capturing complex, non-linear relationships in the data, and Gradient Boosting Machines (GBM) for improving prediction accuracy through iterative refinement of the model. By combining these diverse methodologies, the app achieves a high degree of precision in identifying and classifying a wide array of emotions from the EEG signals.

[0166] The ability to track a comprehensive range of emotions allows the application to provide users with detailed insights into their emotional patterns and triggers. Users can view their emotional history through intuitive visualizations, helping them understand how their emotions fluctuate over time and in response to different activities or events. This feature not only aids in self-awareness but also empowers users to take proactive steps in managing their mental health, such as adopting coping strategies or seeking support when necessary.

[0167] F urthermore, the integration of the circumplex model into the social network feature enhances the community support system. Friends and family members can receive notifications not only about high arousal or anxiety but also about other significant emotional changes. For instance, if a user consistently experiences low arousal and negative valence, indicating potential depression, their network can be alerted to provide timely support. This holistic approach ensures that users are supported in managing a broad spectrum of emotional challenges, fostering a more resilient and emotionally balanced community.

[0168] The present example implementation leverages advanced machine learning techniques to map EEG signals to a wide range of emotions based on the Russell's circumplex model. By incorporating SVM, Random Forest, and other ML classifiers, the app provides precise and comprehensive emotional tracking. This innovation enhances both individual self-awareness and community support, offering a powerful tool for improving emotional wellbeing and resilience.

[0169] In on embodiment, the machine learning (ML) algorithms comprise one or a plurality of the following algorithms: Support Vector Machines (SVM), Random Forest, Neural Networks, Gradient Boosting Machines (GBM), K-Nearest Neighbors (KNN), Decision Trees, Logistic Regression, Naive Bayes, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) Networks, Extreme Gradient Boosting (XGBoost), LightGBM, AdaBoost, Multi-Layer Perceptron (MLP), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Ensemble Methods, Bayesian Networks, Autoencoders, Support Vector Regression (SVR), Fuzzy Logic Systems, Genetic Algorithms, Deep Belief Networks (DBN), Stochastic Gradient Descent (SGD), Elastic Net Regression, Ridge Regression, Lasso Regression

[0170] The Al Neuro-Coach is a groundbreaking system designed to integrate and correlate activities from a user’s smartphone with their brainwaves, providing actionable recommendations to enhance mental wellness on a daily basis. This advanced system operates through a mobile application that measures brainwaves using any existing EEG (electroencephalography) consumer device. Additionally, the example implementation is the first truly wearable EEG device, which comes in the form of a head strip that can retrofit to any headwear, allowing for seamless integration into the user's daily attire.

[0171] The system employs several key metrics to measure different aspects of brain activity and emotional states. To measure arousal, the system uses FABR (Frontal Alpha to Beta Ratio) and FAGR (Frontal Alpha to Gamma Ratio). These metrics are calculated by analyzing the ratio of alpha waves to beta and gamma waves at the frontal regions of the brain. Similarly, the system uses TABR (Temporal Alpha to Beta Ratio) and TAGR (Temporal Alpha to Gamma Ratio) to assess arousal by evaluating the ratio of alpha waves to beta and gamma waves at the temporal regions.

[0172] To measure valence, which refers to the positivity or negativity of an emotional state, the system utilizes FGA (Frontal Gamma to Alpha Ratio) and FB A (Frontal Beta to Alpha Ratio). These ratios provide insights into the balance of brainwave activity in the frontal regions, which is critical for understanding emotional responses.

[0173] In addition to these EEG-based metrics, the Al Neuro-Coach incorporates other physiological biomarkers such as Heart Rate Variability (HRV) and Electrodermal Activity (EDA). HRV measures the variation in time between heartbeats, which is a key indicator ofstress and relaxation levels. EDA measures the skin's conductance, which varies with sweat gland activity and is an indicator of emotional arousal.

[0174] The mobile app features a journaling tab that tracks the user's emotional states throughout the day. This tab includes an activity ring that visually represents the user’s engagement in various activities and a circular graph that dynamically indicates the level of arousal. This visual feedback helps users monitor their emotional states in real-time and make informed decisions about their mental wellness.

[0175] By integrating these advanced metrics and providing continuous feedback, the Al Neuro-Coach empowers users to proactively manage their mental health. The system's ability to correlate smartphone activities with brainwave data offers a holistic approach to mental wellness, ensuring that users receive personalized and effective recommendations to enhance their overall well-being.

[0176] The primary aim of this aspect of the Al Neuro-Coach is to compare the accuracy of arousal measurements computed by machine learning (ML) classifiers against a proprietary formula. This comparison is based on the current subset of brainwave features used by the app to measure arousal.

[0177] To achieve this aim, a detailed methodology is employed involving user interaction with the mobile application. Initially, the subject is presented with a slider on the first screen of the app to provide a self-reported measure of their arousal level. As the subject moves the slider left or right, the arousal value is displayed on a Likert scale with the following points: Very calm (Low Arousal), Calm, Slightly calm, Neutral, Slightly Alert, Alert, and Very alert (High Arousal). While the subject sees only the seven points on the Likert scale, the selectable values range from 1.0 to 10.0, allowing for nuanced self-reporting.

[0178] After the self-report is provided by the subject, a new slider is shown on the screen displaying the Computed Arousal estimated by the app. The app calculates arousal using several brainwave features, including FABR (Frontal Alpha to Beta Ratio) with specific measurements at AF7 and AF8, FAGR (Frontal Alpha to Gamma Ratio) at AF7 and AF8, TABR (Temporal Alpha to Beta Ratio) at TP9 and TP10, and TAGR (Temporal Alpha to Gamma Ratio) at TP9 and TP 10. These features are processed by the app according to a specific formula, ensuring that computed arousal values are real numbers ranging from 1.0 (low arousal) to 10.0 (high arousal).

[0179] The formula ensures that arousal values are normalized between 1.0 and 10.0. If the current value falls outside the predefined range, it is updated dynamically. Features are normalized by taking the maximum and minimum values, recalculating every second, andensuring constant normalization. This dynamic adjustment maintains the accuracy and reliability of the computed arousal values.

[0180] Once the self-report value is selected, both self-reported and computed arousal values are sent to the cloud. Data is stored in .txt files with the following format for each line: “HH:MM:SS self-report measured.” The directory structure in the cloud includes a folder for each subject, containing separate .txt files for different brainwave features and days. This organized data storage facilitates efficient data retrieval and analysis.

[0181] Accuracy in predicting self-reported arousal is computed using the formula:where ACC represents accuracy in the range of 0% to 100%, Max is 10.0, Min is 1.0, S is the self-report value, and M is the measured value. This formula is averaged over all timestamps to obtain the average accuracy for each subject. This rigorous analysis ensures a comprehensive assessment of the Al Neuro-Coach's performance in accurately measuring arousal.

[0182] By comparing the machine learning classifiers and the proprietary formula, the methodology aims to validate the reliability and precision of the app's arousal measurements. This approach not only provides users with accurate insights into their mental states but also enhances the overall effectiveness of the Al Neuro-Coach in promoting mental wellness. Through continuous data collection, normalization, and analysis, the system ensures that users receive timely and accurate feedback, empowering them to make informed decisions about their mental health and well-being.

[0183] Another exemplary embodiment focuses on measuring cognitive load using Al and EEG technology. One of the key methodologies employed is Sequential Feature Selection (SFS) for machine learning classifiers, specifically Support Vector Machines (SVM) and K- Nearest Neighbors (KNN). This process involves selecting a subset of relevant features and setting the number of input and output features, optimizing the classifier's performance in measuring cognitive load.

[0184] In the context of brainwave features, the example implementation utilizes metrics such as Alpha / Beta Ratio (ABR), Temporal Alpha Ratio (TAR), and Temporal Beta Ratio (TBR). These metrics are particularly useful for neurosurgeons in training, providing insights during both simple and complex exercises. One specific brainwave feature highlighted is therelative theta wave activity across all brainwaves in the occipital region, which is crucial for understanding cognitive load.

[0185] The example implementation outlines scenarios where real-time machine learning (ML) is not necessary, given that certain conditions are fixed. These conditions include the specific application of the EEG device, the subject being monitored, and the type of electrodes used. Under these circumstances, pre-trained models can be effectively utilized without the need for real-time adaptation. However, real-time ML becomes essential in specific situations. For example, the features need to be adapted based on the application, such as distinguishing between a neurosurgeon and a pilot. Additionally, the system can be personalized for individual users by collecting data over time and dynamically adapting the models. This approach also allows for adjustments based on the type of electrodes used, whether they are dry or wet.

[0186] The example implementation also discusses the use of wearable EEG devices with eight or any number of electrodes. The frontal electrode configuration has been successfully tested, with the Relative Alpha Band performing well. Although the temporal configuration has not been measured, the occipital electrodes have shown significant effectiveness in capturing relevant brainwave data.

[0187] Overall, the example implementation provides a comprehensive system for measuring cognitive load using advanced machine learning techniques and EEG technology. By optimizing feature selection and adapting models in real-time, it ensures accurate and personalized assessments for various applications, enhancing both training and performance monitoring.

[0188] Another exemplary embodiment describes a novel approach to measuring thermal comfort through EEG signals, specifically by analyzing the Delta_dB and Gamma_dB brainwave bands. This measurement is conducted using electrodes placed on the frontal and temporal regions of the brain, capturing data in epochs of two seconds. The system classifies the thermal comfort levels into three distinct categories based on the amplitude of the Delta and Gamma waves: a high level indicating a warm feeling, a medium level indicating a cold feeling, and a small level indicating a neutral thermal state. This allows for real-time monitoring and assessment of an individual's thermal comfort, providing actionable feedback for environmental adjustments.

[0189] In addition to thermal comfort, the example implementation utilizes entropy as a metric for assessing stress and calmness. Entropy, in this context, refers to the complexity and variability of the EEG signal. During states of stress and discomfort, the entropy of the EEG signal tends to be lower, with a predominance of high-frequency components. Conversely, instates of calmness, the entropy is higher, and there is a predominance of low-frequency components. This method provides a quantitative measure of an individual's mental state, allowing for the detection and differentiation of stress and calmness based on EEG data. By integrating these entropy measurements with other biometric data, the system offers a comprehensive tool for monitoring and improving mental wellness.

[0190] The example implementation also encompasses a novel application for detecting and monitoring neurodegenerative conditions, particularly Multiple Sclerosis (MS). MS is often associated with optical neuritis, which can present in two forms: recurrent, where the condition occurs continuously, and acute, where it occurs sporadically with peaks. Detection of optical neuritis traditionally involves a 90-second EEG test using three electrodes placed at Oz, Cz, and Fpz. During this test, latency between a visual stimulus and the signal at the optical nerve is measured. The stimulus can be a flashing light or a chessboard pattern that inverts colors at different frequencies. For a healthy subject, the latency is typically around 104 milliseconds, whereas an MS subject on medication shows a latency between 112-130 milliseconds, and an MS subject off medication shows a latency of approximately 185 milliseconds.

[0191] One embodiment of the example implementation leverages its portability and integration with a smartphone to provide early detection of MS by measuring optical neuritis. This is particularly beneficial in cases of acute neuritis, where traditional methods might miss sporadic peaks due to their brief testing duration. Continuous wear of the device allows for early detection, capturing neuritis symptoms long before they would be identified in a lab setting. The algorithm employed for this application comprises of latency analysis, where a visual stimulus is displayed on the smartphone and the delay in the optical nerve response is measured. The subject is asked to open and close their eyes, and synchronization of eye blinking with the delay is detected using EEG. Spectral analysis is also conducted to measure beta waves, which can indicate tendencies towards tiredness and distraction earlier than in normal patients. Although this measure can sometimes be ambiguous, when combined with latency analysis, it provides a more comprehensive assessment. Additionally, entropy analysis is used, as changes in entropy can differ between normal subjects and those with MS.

[0192] The example implementation further extends its capabilities to detect and monitor other neurodegenerative conditions such as Alzheimer's and Parkinson's disease, as well as seizures. For these conditions, transcranial electrical stimulation is employed. This method involves applying electrical current to specific brain areas affected by these conditions. The approach is still in preliminary stages, as it is not entirely clear why the current is effective.The current is adapted based on the specific pathology, whether it is Multiple Sclerosis, Alzheimer's, or Parkinson's disease.

[0193] In cases of seizures, the wearable device can continuously monitor brain activity, providing early warnings of potential seizures. By analyzing the EEG data in real-time, the system can detect patterns that precede seizures, allowing for timely intervention. Overall, this embodiment showcases the versatility and potential of the example implementation in providing continuous, real-time monitoring and early detection of a range of neurodegenerative conditions, enhancing patient care and treatment outcomes.

[0194] In one embodiment of the example implementation, the EEG wearable device coupled with the Ai neuro-coach app comprises of one or a plurality of alarm systems to warn the user of any of the event disclosed in the present example implementation including but not limited to high / low arousal, high-low valance, high anxiety or stress, imminent seizure, initial sign of any neurodegenerative diseases. As the event is detected, the EEG device would produce one or a plurality of the following types of alarms including but not limited to: sounds, vibration, LED lights, flashing screen, text notifications, voice alerts, haptic feedback, visual alerts, auditory alarms, tactile feedback, ringtone alerts, beeping sounds, chimes, buzzers, blinking icons, push notifications, email alerts, app notifications. In another embodiment, the same alarms are produced by the smartphone connected to the EEG wearable device via Bluetooth.

[0195] One embodiment of the example implementation is an EEG device integrated into a necklace, This innovative design offers a unique and discreet method for monitoring brain activity and other physiological signals, providing a wearable solution that seamlessly blends with eveiyday accessories. The necklace is designed to incorporate advanced sensing technology, specifically a ferrite toroid with coils, which is capable of detecting signals from the vagus nerve and sympathetic nerves.

[0196] The ferrite toroid functions by detecting the electrical signals associated with the parasympathetic and sympathetic responses. The parasympathetic response, managed by the vagus nerve, is crucial for regulating rest and digestion activities, while the sympathetic nerves are responsible for the body's fight-or-flight responses. By measuring the activity of these nerves, the necklace can provide valuable insights into the user's stress levels, relaxation states, and overall autonomic nervous system balance.

[0197] In addition to monitoring brain activity through traditional EEG signals, the necklace's ability to capture data from the vagus and sympathetic nerves enhances its functionality. This dual capability allows for a more comprehensive assessment of the user'sphysiological state. The collected data can be transmitted wirelessly to a paired mobile application, where sophisticated algorithms analyze the information and provide real-time feedback. This feedback can include recommendations for stress management, relaxation techniques, and other interventions to improve mental and physical well-being.

[0198] The necklace is designed with user comfort and aesthetics in mind. It features a stylish and ergonomic design that ensures it can be worn throughout the day without discomfort. The integration of advanced sensor technology within a common accessory makes it an unobtrusive and appealing option for continuous physiological monitoring. This embodiment of the EEG device represents a significant advancement in wearable technology, offering a practical and elegant solution for enhancing mental health and overall well-being.

[0199] One embodiment of the example implementation involves the use of various magnetic materials to enhance the functionality of the EEG device integrated into a necklace. The necklace utilizes magnetic materials such as ferrite, neodymium, samarium-cobalt, and alnico, which are chosen for their distinct magnetic properties and ability to interact with electromagnetic fields. Ferrite materials, in particular, are highly suitable due to their high magnetic permeability and low electrical conductivity, making them effective for detecting and managing electromagnetic signals.

[0200] The necklace is primarily constructed from ferrite toroids embedded with coils, designed to detect the magnetic fields produced by the current flowing along the vagus nerve. When positioned around the neck, these ferrite toroids can absorb the magnetic field generated by the bioelectric activity of the vagus nerve, which is a critical component of the parasympathetic nervous system. The interaction between the magnetic field and the ferrite material induces a current within the coils of the necklace. This induced current can be captured and analyzed to decode the signals emanating from the vagus nerve. By capturing the bioelectric signals through this method, the necklace provides a non-invasive way to monitor the autonomic nervous system's activity . The vagus nerve plays a crucial role in regulating various bodily functions, including heart rate, digestion, and respiratory rate. By analyzing the signals from the vagus nerve, the necklace can infer the wearer’s emotional state, providing insights into levels of stress, relaxation, and overall emotional well-being.

[0201] The magnetic properties of the materials used in the necklace are essential for its functionality. Ferrite materials are known for their excellent magnetic performance at high frequencies, making them ideal for detecting subtle changes in the bioelectric signals of the vagus nerve. Neodymium and samarium-cobalt magnets, known for their high magnetic strength, can also be used to enhance the sensitivity of the device. Alnico magnets, with theirstable magnetic properties over a wide range of temperatures, contribute to the reliability and accuracy of the signal detection.

[0202] Overall, the use of these magnetic materials in the necklace design allows for an effective and innovative method of monitoring the autonomic nervous system. By decoding the magnetic fields induced by the bioelectric activity of the vagus nerve, the necklace can provide real-time feedback on the wearer’s emotional state. This capability makes it a powerful tool for mental health monitoring, offering users valuable insights into their physiological and emotional well-being.

[0203] The user interface (UI) of the application is meticulously designed to enhance user engagement and experience. One of the standout features is the dynamic design of the Selfreport and Live-feed tabs, where the buttons change colors according to the mood selected by the user. This intuitive design helps users quickly associate their mood entries with corresponding visual cues, making it easier to track emotional changes and patterns over time. By incorporating this feature, the app ensures that users have a seamless and visually appealing experience, which is crucial for regular and long-term use.

[0204] To further personalize the user experience, the app allows users to add a photo to their avatar. This small but significant feature creates a more engaging and relatable interface, encouraging users to interact more with the app. Personalization elements like this are essential in building a connection between the user and the application, fostering a sense of ownership and commitment to their mental health journey.

[0205] The calendar within the app is color-coded to provide a dear and quick overview of daily brain activity patterns. Each day is represented by a single pure color that summarizes the brain activity for that day. This color-coding system offers users an immediate visual summary of their emotional and cognitive states, allowing them to easily identify trends and significant changes over time. Such a visual representation simplifies the interpretation of complex data, making it accessible even to users without a background in neuroscience.

[0206] Another innovative aspect of the app is its visualization of brain signals. The UI displays these signals in a manner similar to a seismograph, which records seismic activity on paper. This design choice not only makes the data visually engaging but also intuitive to interpret. By representing neural activity in this familiar format, the app makes it easier for users to understand the fluctuations and patterns in their brainwaves, enhancing their ability to correlate these patterns with their daily experiences and moods. In another embodiment, the app shows the brain map at different brainwave bands, including but not limited to delta, theta, alpha, beta, gamma.

[0207] The mood tracking feature is central to the app's functionality, allowing users to log and monitor their emotional states over time. This capability helps users identify patterns and correlations between their brain activity and their moods, providing valuable insights into their emotional well-being. By consistently tracking their mood, users can become more aware of how various factors impact their mental state, empowering them to make informed decisions about their mental health.

[0208] The app also includes a social campaign component, encouraging users to share their brain activity insights and mood patterns with friends and family. This feature fosters a community of support and awareness, where users can connect and learn from each other's experiences. Sharing insights can help users feel less isolated in their mental health journey and can promote collective learning and encouragement.

[0209] In addition to its interactive features, the app provides in-app tutorials and articles from experts to help users understand and utilize the app's features effectively. These resources ensure that users are well-informed about how to interpret their brain activity data and leverage the app for optimal mental health benefits. The educational content helps demystify the technology and science behind the app, making it more approachable and user-friendly.

[0210] To ensure accuracy, the app periodically asks users how they are doing to help calibrate the device. This input is crucial for the app to accurately reflect the user's current brain state and mood, enhancing the reliability of the data collected. Regular calibration through user input helps maintain the precision of the app's readings and recommendations.

[0211] The app provides live brainwave displays, showing the user's current brain state as they wear the device. This real-time feedback can be invaluable for users looking to understand their immediate cognitive and emotional states. However, the app also offers the option to hide this feature, allowing users to reduce distractions and focus on other tasks when needed. This flexibility ensures that the app can be tailored to fit the user's preferences and lifestyle, making it a versatile tool for mental health management.

[0212] Demographic and Lifestyle Data Collection: the example implementation encompasses an innovative system that leverages demographic and lifestyle data collection to develop personalized recommendations for enhancing mental well-being. The mobile application at the core of this system allows users to record a comprehensive range of demographic and lifestyle information, which forms the foundation for creating highly individualized feedback and suggestions. The data collected includes crucial aspects such as gender, year of birth, and dominant hand, which can influence a person's cognitive andemotional responses. By capturing this information, the application can provide tailored recommendations that resonate with the user's unique physiological and psychological makeup.

[0213] In addition to basic demographic details, the application also gathers data on the user's physical activity level, which is a critical component in understanding overall health and its impact on mental well-being. Regular physical activity is known to have profound effects on mood and stress levels, and by monitoring these activities, the app can offer insights into how exercise patterns correlate with emotional states. This data helps the app to suggest appropriate physical activities that can enhance the user's mental health, whether it be recommending more frequent exercise sessions or identifying the most beneficial types of physical activities.

[0214] Education level is another significant data point collected by the application. The user's educational background can influence their cognitive processes, stress management techniques, and overall mental health. By understanding the user's education level, the app can provide more relevant and accessible mental wellness strategies. For instance, users with higher educational attainment might prefer detailed, research-based recommendations, while those with less formal education might benefit from more straightforward, practical advice.

[0215] The application also takes into account language ability and musical ability, recognizing that these skills can pl ay a role in mental wellness. Language proficiency can affect how users process information and communicate their needs, so the app can tailor its communication style accordingly. Musical ability, on the other hand, has been linked to enhanced cognitive functions and emotional regulation, The app can leverage this data to recommend music-related activities or therapies that align with the user's abilities and preferences, thereby promoting mental well-being.

[0216] Regular wellness practices are another critical aspect of the data collection process. This includes habits such as meditation, yoga, or other relaxation techniques that the user might regularly engage in. By understanding the user's existing wellness practices, the application can integrate these into its recommendations and enhance them with new, evidence-based strategies. This personalized approach ensures that the user receives advice that is not only relevant but also easily implementable within their current lifestyle.

[0217] The detailed demographic and lifestyle data collected is analyzed using advanced machine learning models. These models are designed to identify patterns and correlations within the data, allowing the application to generate highly personalized recommendations. The integration of machine learning ensures that the app's feedback is dynamic and continually refined based on the user's ongoing data inputs. As the user continues to interact with the appand update their information, the recommendations become more accurate and effective, fostering an ever-evolving support system for mental well-being.

[0218] This sophisticated demographic and lifestyle data collection system allows the mobile application to offer personalized, actionable insights that cater to the unique needs of each user. By leveraging machine learning models to analyze a broad spectrum of personal data, the application provides tailored recommendations that promote mental wellness in a holistic and highly effective manner. This approach not only enhances the user experience but also significantly improves the potential for positive mental health outcomes.

[0219] Social Connection: the present example implementation introduces an innovative feature focused on enhancing social connections through the use of a mobile application. This feature leverages advanced algorithms to identify moments when the user may benefit from social interaction and provides timely suggestions for contacting friends. By doing so, the application not only supports the user's mental well-being but also fosters a supportive community around them.

[0220] The core of this example implementation lies in its ability to monitor the user's emotional state continuously through wearable EEG technology and other integrated sensors. By analyzing brainwave patterns and other physiological data, the application can detect signs of emotional distress, loneliness, or heightened stress. When such patterns are identified, the application suggests reaching out to a friend or family member in real-time. This prompt is designed to encourage the user to seek social support when it is most needed, thereby preventing the escalation of negative emotions and promoting a sense of connectedness.

[0221] In addition to suggesting social interaction, the application can proactively notify selected friends or family members when the user may need support. This is particularly useful in scenarios where the user might not feel comfortable or capable of reaching out themselves. The application sends a discreet notification to the chosen contacts, informing them that the user might benefit from a check-in. This feature ensures that the user receives timely support from their social network, reinforcing the importance of community in maintaining mental well-being.

[0222] The integration of this social connection feature with the user's existing network is seamless. Users can select trusted friends and family members who will receive notifications. They can also customize the frequency and conditions under which these notifications are sent, ensuring that the feature respects their privacy and preferences. This personalized approach ensures that the support system is both effective and non-intrusive.

[0223] Moreover, the application includes features that facilitate easy communication with these contacts. For instance, it can integrate with messaging apps, email, and social media platforms to streamline the process of reaching out. Users can send predefined messages or customize their communications, making it easier to express their needs. This functionality reduces the barriers to seeking help and enhances the likelihood of timely social interactions.

[0224] The application also includes a feedback mechanism that allows users to report on the effectiveness of the social interactions facilitated by the app. This feedback is used to refine the algorithms further, ensuring that the recommendations for social connections become more accurate and beneficial over time. By continuously learning from user interactions, the application enhances its ability to provide meaningful support.

[0225] In addition to real-time social interaction prompts, the application offers features for long-term social engagement. It can suggest activities that users can enjoy with their friends and family, such as virtual meetups, shared hobbies, or group wellness activities. These suggestions are tailored to the user's preferences and the interests of their social network, fostering a deeper and more consistent connection with their community.

[0226] This example implementation represents a significant advancement in the integration of social support with mental health technology. By identifying moments when social interaction is beneficial and facilitating timely communication, the application enhances the user's emotional well-being and strengthens their social network. This proactive approach to social connection ensures that users receive the support they need when they need it most, fostering a community-centric approach to mental wellness.

[0227] Integration with Scheduling Tools: The present example implementation introduces an advanced feature that integrates seamlessly with popular scheduling tools such as Google Calendar, Apple Calendar, and Microsoft Outlook. This integration allows the application to correlate scheduled activities with the user's stress responses, providing valuable insights into how different tasks and commitments impact their emotional well-being. By leveraging machine learning models and artificial intelligence, the application can identify patterns and offer predictive recommendations to help users manage their stress more effectively.

[0228] This integration begins with the synchronization of the user’s calendar data with the application. The app retrieves details of upcoming meetings, appointments, and other scheduled activities, aligning them with real-time data on the user’s emotional and physiological states. This synchronization is continuous, ensuring that the app always has the most up-to-date information about the user’s schedule.

[0229] Once the calendar data is integrated, the application employs machine learning algorithms to analyze the correlation between the user's scheduled activities and their stress responses. For instance, it can identify patterns such as increased stress levels during back-to- back meetings, or lower stress levels during breaks and leisure activities. By understanding these patterns, the application can provide the user with actionable insights into how their schedule affects their mental well-being.

[0230] One of the key benefits of this integration is the app's ability to make predictive recommendations. Using historical data and real-time monitoring, the app can forecast periods of high stress based on the user's upcoming schedule. For example, if the app detects that the user has a series of intense meetings lined up, it might suggest incorporating short breaks or mindfulness exercises between sessions to mitigate stress. These recommendations are personalized, taking into account the user's unique stress patterns and preferences.

[0231] The integration with scheduling tools also allows the application to offer proactive alerts and reminders. If the app predicts a potential spike in stress due to a particularly demanding day, it can send notifications to the user suggesting preemptive stress-relief activities, such as a brief meditation session or a walk. These timely interventions help the user manage their stress before it escalates, promoting better emotional regulation throughout the day. Furthermore, the application provides detailed reports and visualizations that help users understand the relationship between their schedule and their stress levels. These reports can include graphs and charts that highlight periods of high and low stress, correlating them with specific activities. This visual feedback enables users to make informed decisions about how to structure their time more effectively, enhancing their overall productivity and well-being.

[0232] The app’s machine learning models continuously learn and adapt to the user’s behavior, improving the accuracy of its predictions and recommendations over time. This dynamic learning process ensures that the app remains responsive to changes in the user’s lifestyle and stress patterns, offering increasingly precise and relevant advice.

[0233] In addition to personal use, this integration can be beneficial in professional settings. For instance, managers can use aggregated and anonymized data to understand the stress patterns of their teams, allowing them to implement policies that promote a healthier work environment. By identifying peak stress periods across the team, managers can adjust meeting schedules, encourage regular breaks, and introduce wellness programs that align with the team’s needs.

[0234] The integration of the application with scheduling tools like Google Calendar, Apple Calendar, and Microsoft Outlook represents a significant advancement in stressmanagement technology. By correlating scheduled activities with stress responses and using Al-driven predictive recommendations, the app empowers users to take control of their emotional well-being. This innovative approach not only enhances individual stress management but also fosters a more balanced and productive lifestyle.

[0235] Integration with Collaboration and Productivity Tools: The present example implementation introduces an innovative integration of the application with various collaboration and productivity tools such as Slack, Microsoft Teams, and Notion. This seamless integration incorporates real-time feedback into the user's workflow, allowing for the scheduling of breaks and other stress-relief activities without requiring the user to leave their primary working tools. This ensures minimal disruption to the user's workflow while promoting better mental well-being.

[0236] This integration begins with the application connecting to the user’s preferred collaboration and productivity tools. By leveraging APIs and other integration methods, the application can access the user's work environment, including communication channels, project management boards, and task lists. This connection allows the application to monitor the user's activities and interactions, providing a comprehensive view of their workload and stress levels.

[0237] One of the primary features of this integration is the ability to schedule breaks and stress-relief activities directly within the collaboration and productivity tools. For example, if the application detects that the user has been engaged in continuous work for an extended period, it can automatically schedule a break in the user's calendar or task list. This break is accompanied by a notification in the collaboration tool, reminding the user to take a moment to relax and recharge. This proactive approach helps prevent burnout and promotes regular intervals of rest throughout the workday .

[0238] Moreover, the application can provide real-time feedback and recommendations based on the user's current stress levels and workload. For instance, if the user is in the middle of a high-stress project, the application can suggest short mindfulness exercises or breathing techniques that can be done without leaving the desk. These recommendations are delivered through the collaboration tools, ensuring that the user can access them easily and without interrupting their workflow.

[0239] The integration also allows for the customization of notifications and reminders. Users can set preferences for the type and frequency of alerts they receive, ensuring that the recommendations fit seamlessly into their work routine. For example, a user might prefer to receive reminders for a five-minute stretch every hour or a brief meditation session aftercompleting a significant task. This flexibility ensures that the stress-relief activities are personalized and relevant to the user's needs.

[0240] In addition to individual stress management, this integration can also benefit teams and organizations. Managers can use aggregated and anonymized data to understand the stress patterns of their team members. This insight allows managers to implement policies and practices that promote a healthier work environment. For example, if the data shows that team members experience high stress during certain types of meetings or projects, managers can adjust workflows or introduce additional support during those times.

[0241] Furthermore, the integration can enhance team collaboration by promoting shared wellness practices. For instance, the application can suggest team-wide breaks or mindfulness sessions, encouraging collective participation in stress-relief activities. These shared experiences can foster a sense of community and support within the team, enhancing overall morale and productivity.

[0242] Another significant advantage of this integration is its potential to streamline communication about well-being within the workplace. Employees can use the application to signal when they are feeling overwhelmed or stressed, prompting supportive responses from colleagues or managers. This feature can help create a more open and empathetic work culture, where employees feel comfortable discussing their mental health and seeking support when needed.

[0243] The integration with collaboration and productivity tools also ensures that the application’s features are accessible to users throughout their workday. By embedding stress management recommendations and activities within the tools they use daily, the application ensures that users do not need to switch contexts to take care of their mental well-being. This seamless experience encourages consistent use of the application’s features, leading to better long-term outcomes for stress management and emotional health.

[0244] The integration of the application with collaboration and productivity tools such as Slack, Microsoft Teams, and Notion represents a significant advancement in workplace wellness technology. By incorporating real-time feedback and stress-relief recommendations directly into the user’s workflow, this integration promotes better mental well-being without disrupting productivity . It provides personalized, actionable insights that help users manage their stress levels effectively while fostering a supportive and empathetic work environment.

[0245] Localization of Senices: the present example implementation introduces an advanced feature within the mobile application that focuses on the localization of senices to enhance the user’s mental well-being. This feature identifies nearby services that offer activitiesknown to provide stress relief or act as positive activators for the user. The types of services located by the app include gyms, parks, acupuncture centers, wellness centers, and churches. By leveraging geolocation technology, the app can provide users with a list of accessible and beneficial activities tailored to their specific needs and preferences.

[0246] Upon detecting elevated stress levels or the need for a mental boost, the application automatically scans the user’s vicinity for relevant services. This process begins with the app analyzing the user's current location using GPS data. Once the location is determined, the app queries a comprehensive database of local services that offer stress-relief activities. This database is continuously updated to ensure the information remains accurate and relevant.

[0247] The user interface presents these localized options in a clear and user-friendly manner. Each service is listed with key details, including the type of activity, distance from the user, and estimated time required for the activity. For instance, if a user is feeling stressed and the app identifies a nearby park, it might suggest a 20-minute walk as a stress-relief activity, complete with walking directions and the estimated time to and from the location, This personalized recommendation not only helps the user to manage stress effectively but also integrates seamlessly into their daily routine.

[0248] Moreover, the application provides additional information about each service to help users make informed decisions. For example, details about the facilities available at a gym, the qualifications of practitioners at an acupuncture center, or the schedule of services at a church can be included. This information empowers users to choose the most suitable option for their needs, enhancing their overall experience and satisfaction.

[0249] The app's ability to offer personalized recommendations is further refined through the use of machine learning algorithms. By analyzing the user's past behaviors and preferences, the app can predict which activities are most likely to be effective for them. For example, if the user frequently benefits from yoga sessions, the app will prioritize nearby wellness centers that offer yoga classes. This level of personalization ensures that the recommendations are not only relevant but also highly effective in reducing stress and improving mental well-being.

[0250] Integration with the app’s other features enhances the functionality of the localized services. For instance, after suggesting a visit to a nearby park, the app might remind the user to log their mood before and after the walk. This logging feature helps users to track the impact of different activities on their stress levels, providing valuable insights into what works best for them. Over time, this data contributes to a more tailored and effective stress management plan.

[0251] In addition to individual benefits, the localization feature can foster community engagement. By highlighting local wellness events or group activities, the app encourages users to participate in community-based stress-relief activities. This social aspect not only helps in managing stress but also builds a sense of belonging and support within the community. For example, the app might suggest a community yoga session or a mindfulness workshop at a local wellness center, promoting both physical and social well-being.

[0252] Furthermore, the app includes an estimated time component for each activity, helping users to plan their day more effectively. For instance, if a user has a tight schedule but needs a quick stress-relief activity, the app might suggest a brief meditation session at a nearby wellness center that takes only 10 minutes. This practical approach ensures that users can incorporate stress-relief activities into their busy schedules without feeling overwhelmed or disrupted.

[0253] The localization of services feature within the mobile application represents a significant advancement in personalized stress management and mental well-being. By leveraging geolocation technology and machine learning algorithms, the app identifies nearby services that offer effective stress-relief activities tailored to the user's needs. This feature enhances the user experience by providing detailed information, personalized recommendations, and seamless integration with the app’s other functionalities. It promotes individual well-being, fosters community engagement, and ensures that users can manage their stress effectively within the context of their daily lives.

[0254] Integration with Health Data Orchestrators: The present example implementation introduces a sophisticated integration with health data orchestrators such as Apple Health and other similar platforms. This feature enables the mobile application to seamlessly gather and correlate physical activity data and other biometric information with the user’s emotional responses. By leveraging this integration, the application can provide more comprehensive insights into the user’s overall well-being and offer personalized, predictive recommendations to enhance mental health management

[0255] Integrating with health data orchestrators allows the application to access a wealth of biometric data that users already collect through their devices. This data includes, but is not limited to, heart rate, sleep patterns, physical activity levels, body temperature, and calorie intake. By combining this information with the emotional data collected through the wearable EEG device, the application can identify patterns and correlations that would otherwise be difficult to detect.

[0256] For example, the application can analyze how fluctuations in heart rate correlate with periods of high stress or anxiety. If a user experiences elevated heart rate consistently during specific activities or times of the day, the application can recognize these patterns and provide tailored recommendations to manage these stressors. This might include suggesting relaxation techniques, adjusting the user's schedule to include more breaks, or recommending specific physical activities known to reduce stress.

[0257] Machine learning models play a crucial role in this integration. By utilizing advanced algorithms, the application can analyze the vast amounts of data collected and identify significant patterns and trends. These machine learning models are trained to recognize the complex interactions between physical and emotional health metrics, allowing the application to offer highly accurate and personalized feedback. For instance, the application might learn that a user’s emotional state improves significantly with a certain level of physical activity, prompting it to suggest a daily exercise routine tailored to their needs.

[0258] The integration also facilitates the use of Al for predictive recommendations. By continuously monitoring and analyzing the data, the application can anticipate potential stressors or emotional challenges before they become significant issues. For example, if the data indicates that a user’s sleep quality is deteriorating and correlating with increased stress levels, the application can proactively recommend interventions to improve sleep hygiene and reduce stress. This predictive capability empowers users to take preemptive actions to maintain their mental well-being, rather than merely reacting to problems as they arise.

[0259] Furthermore, this integration enhances the application's ability to provide real-time feedback. By continuously updating the user’s profile with the latest biometric data, the application ensures that the recommendations and insights are based on the most current information available. This real-time analysis is particularly beneficial for users with fluctuating health conditions, as it allows them to make informed decisions about their activities and routines on a day-to-day basis.

[0260] The integration with health data orchestrators also supports a more holistic approach to mental well-being. By considering a wide range of health metrics, the application can provide a more comprehensive understanding of the factors influencing the user's emotional state. This holistic perspective enables the application to offer multifaceted recommendations that address various aspects of the user’s lifestyle, from physical activity and diet to sleep and stress management techniques.

[0261] Moreover, the application ensures user privacy and data security through robust encryption and secure data handling practices. Users have control over which data is sharedand can customize their privacy settings to suit their comfort levels. This transparency and control help build trust, encouraging more users to take advantage of the application’s comprehensive health management features.

[0262] In addition to individual benefits, the integration with health data orchestrators can contribute to broader health research and insights. Aggregated, anonymized data from multiple users can be used to identify population-level trends and correlations, providing valuable information for public health initiatives and scientific research. This collective data analysis can lead to new discoveries and improved strategies for managing mental and physical health on a larger scale.

[0263] The integration with health data orchestrators significantly enhances the capabilities of the mobile application by providing a more detailed and comprehensive view of the user’s health. By correlating physical activity and biometric data with emotional responses, the application can identify patterns, offer predictive recommendations, and support a holistic approach to mental well-being. This integration leverages machine learning and Al to provide personalized, real-time feedback, empowering users to proactively manage their health and improve their overall qualify of life.

[0264] Food Tracker: the present example implementation introduces an innovative Food Tracker feature within the mobile application, designed to correlate food intake, scheduling, and food types with the user's stress responses. This functionality allows for the identification of dietary patterns that impact the user's stress levels, providing personalized insights and recommendations to improve overall mental and physical well-being.

[0265] The Food Tracker enables users to log their daily food intake, including the types of food consumed, portion sizes, and meal times. By capturing detailed information about the user's dietary habits, the application can analyze the relationship between food consumption and emotional states. This data collection is facilitated through an intuitive user interface that allows for quick and easy logging, ensuring that users can consistently track their meals without significant effort.

[0266] By integrating this food tracking data with the emotional and biometric data collected by the wearable EEG device and other sensors, the application can identify patterns and correlations that would otherwise be difficult to discern. For example, the application may detect that certain foods or eating patterns are associated with increased stress levels, while others may correspond to improved emotional stability and reduced anxiety.

[0267] Machine learning algorithms are employed to analyze the collected data, enabling the application to identify significant trends and provide personalized dietaryrecommendations. These algorithms can process large datasets to uncover subtle interactions between food intake and emotional responses. For instance, the application might learn that a user's stress levels tend to spike after consuming high-sugar foods or that certain nutrient-dense meals correlate with periods of calm and focus.

[0268] The Food Tracker feature also includes a scheduling component that correlates meal times with stress responses. By analyzing the timing of food intake in relation to the user's daily schedule and emotional states, the application can suggest optimal meal times to help regulate stress levels. For example, if the data indicates that skipping meals or eating late at night exacerbates stress, the application can recommend more consistent meal scheduling to promote better emotional balance.

[0269] Furthermore, the Food Tracker can identify the impact of specific food types on the user's stress levels. By categorizing foods into various groups, such as high-protein, high- carbohydrate, or high-fat, the application can determine which types of foods have the most significant influence on the user's emotional state. This detailed analysis enables the application to provide targeted dietary advice, such as incorporating more foods rich in omega-3 fatty acids to help manage stress or reducing caffeine intake to improve sleep quality.

[0270] The application also allows users to set dietary goals and receive reminders to help them stay on track with their nutritional plans. These goals can be customized based on the user's preferences and the insights gained from the Food Tracker data For example, the application might suggest increasing the intake of certain foods that have been shown to have a calming effect, such as leafy greens or whole grains.

[0271] In addition to individual dietary recommendations, the Food Tracker feature can provide broader insights into the relationship between diet and mental health. Aggregated data from multiple users can be analyzed to identify population-level trends and inform public health strategies. This collective analysis can contribute to a better understanding of how dietary habits influence stress and emotional well-being, potentially leading to improved nutritional guidelines and interventions.

[0272] fhe integration of the Food Tracker with the application's other features, such as the mood tracking and biometric monitoring, provides a comprehensive approach to health management. By considering the interplay between diet, physical activity, and emotional states, the application can offer holistic recommendations that address multiple aspects of the user's lifestyle, This comprehensive perspective is crucial for developing effective strategies to manage stress and improve overall well-being.

[0273] Moreover, the Food Tracker ensures user privacy and data security through robust encryption and secure data handling practices. Users have control over their dietary data and can customize their privacy settings to suit their comfort levels. This transparency and control help build trust, encouraging more users to take advantage of the application's comprehensive health management features.

[0274] In conclusion, the Food Tracker feature of the mobile application represents a significant advancement in the field of health and wellness technology. By correlating food intake, scheduling, and food types with stress responses, the application can identify dietary patterns that impact emotional well-being. Through detailed data analysis and personalized recommendations, the Food Tracker empowers users to make informed dietary choices that promote better mental and physical health. This holistic approach to health management, supported by machine learning and Al, enhances the user's ability to manage stress and improve their overall quality of life.

[0275] Voice Tracker: the present example implementation introduces a novel Voice Tracker feature within the mobile application, designed to identify tones of voice and inflections associated with specific stress responses. By analyzing vocal characteristics in real time, the application provides immediate feedback to users, contributing to a comprehensive understanding of their emotional states and enhancing their ability to manage stress and emotional well-being.

[0276] The Voice Tracker operates by continuously monitoring the user's speech patterns through the microphone on their smartphone or wearable device. Utilizing advanced signal processing techniques, the application analyzes various aspects of the user's voice, such as pitch, tone, rhythm, and volume. These vocal characteristics can reveal a great deal about the user's emotional state, as changes in voice often correlate with different levels of stress and emotional arousal.

[0277] One of the key innovations of the Voice Tracker is its ability to detect subtle changes in the user's voice that may indicate stress or anxiety. For example, an increase in pitch or a more rapid speech rate can be signs of heightened stress, while a monotonous or flattened tone may indicate low energy or emotional distress. By identifying these vocal markers, the application can provide real-time feedback, alerting the user to their current emotional state and suggesting appropriate actions to manage their stress.

[0278] The real-time feedback provided by the Voice Tracker is presented through the mobile application's user interface. When the application detects stress-related changes in the user's voice, it can prompt the user with notifications or suggestions for stress-relief activities,such as taking a deep breath, engaging in a brief meditation, or stepping away from a stressful situation. This immediate intervention helps users become more aware of their stress levels and take proactive steps to mitigate stress before it escalates.

[0279] Furthermore, the Voice Tracker's analysis is not limited to real-time feedback. The application also logs the user's vocal data over time, allowing for the identification of longterm patterns and trends. By reviewing these logs, users can gain insights into how their vocal expressions correlate with their emotional states and identify recurring triggers or stressors in their daily lives. This historical data can be invaluable for users seeking to understand and improve their emotional resilience.

[0280] The integration of the Voice Tracker with other features of the mobile application enhances its effectiveness. For instance, the vocal data collected by the Voice Tracker can be combined with biometric data from wearable EEG devices, heart rate monitors, and other sensors to provide a more comprehensive picture of the user’s emotional well-being. By correlating vocal characteristics with physiological markers, the application can offer more accurate and personalized recommendations for stress management.

[0281] Additionally, the Voice Tracker can contribute to the development of personalized emotional profiles using machine learning algorithms. By analyzing vocal data alongside other demographic and lifestyle information, the application can refine its understanding of how individual users express stress and tailor its feedback accordingly. This personalized approach ensures that the application's recommendations are relevant and effective for each user.

[0282] The Voice Tracker also supports the application's social connectivity features. For example, if the application detects significant stress in the user's voice, it can suggest reaching out to a friend or family member for support. In cases where the user has granted permission, the application can notify designated contacts about the user's stress levels, facilitating timely social interventions and strengthening the user's support network.

[0283] Privacy and data security are paramount in the design of the Voice Tracker. All vocal data is securely encrypted and stored, with users having full control over their data and the ability to adjust privacy settings as needed. This commitment to privacy ensures that users can confidently use the Voice Tracker without concerns about unauthorized access or misuse of their personal information.

[0284] The Voice Tracker feature represents a significant advancement in the field of emotional well-being technology. By analyzing tones of voice and vocal inflections, the application provides real-time feedback on stress levels, helping users to better understand and manage their emotional states. The integration of vocal data with other biometric and lifestyleinformation enhances the accuracy and personalization of the application's recommendations, contributing to a holistic approach to stress management and emotional health. This innovative feature not only empowers users to take control of their mental well-being but also fosters a deeper connection between vocal expressions and emotional states.

[0285] Impulse shopping: the present example implementation introduces a novel Impulse Shopping feature within the mobile application, designed to identify and mitigate impulse shopping tendencies by correlating shopping patterns with the user's stress levels. This innovative feature integrates with popular online stores such as Amazon, Zalando, and Shein, providing users with real-time feedback and alternative suggestions to promote healthier stressrelief activities.

[0286] Impulse shopping often occurs as a coping mechanism during times of stress or emotional distress. The application continuously monitors the user's shopping behavior by tracking their interactions with online stores, including browsing history, items added to the cart, and purchase history. By analyzing these patterns, the application can detect potential impulse shopping behaviors, particularly those that deviate from the user's typical shopping habits.

[0287] To achieve this, the application utilizes advanced machine learning algorithms that analyze both historical and real-time data. These algorithms can identify spikes in shopping activity that coincide with periods of heightened stress, as measured by wearable devices that monitor physiological markers such as heart rate variability (HRV) and electrodermal activity (EDA). When a correlation is detected, the application generates alerts to notify the user of their potential impulse shopping tendencies.

[0288] When an alert is triggered, the application provides real-time feedback to the user, encouraging them to reconsider their shopping decisions. This feedback includes reminders of their stress levels and suggestions for alternative activities that can help alleviate stress more effectively. For example, the application might suggest taking a walk, practicing mindfulness exercises, or engaging in a hobby. By redirecting the user's focus away from impulsive purchases and towards healthier stress-relief activities, the application helps users develop better coping mechanisms and avoid unnecessary expenditures.

[0289] The Impulse Shopping feature also includes a customizable setting that allows users to set limits on their online shopping activities. Users can define specific parameters, such as a maximum number of purchases per week or a spending cap, and receive alerts when they approach these limits. This proactive approach empowers users to maintain control over their shopping habits and reinforces positive behavior changes.

[0290] In addition to real-time alerts, the application provides users with detailed insights into their shopping behaviors and stress levels over time. These insights are presented through intuitive visualizations that highlight patterns and trends, helping users understand the relationship between their emotional states and shopping habits. By identifying triggers for impulse shopping, users can take preventive measures to manage their stress more effectively.

[0291] The application also supports social connectivity features, allowing users to share their progress with friends and family members. For instance, users can choose to notify trusted contacts when they receive impulse shopping alerts, fostering a supportive environment that encourages accountability and positive reinforcement. Friends and family can provide encouragement and suggestions for alternative stress-relief activities, further enhancing the user's ability to manage their impulses.

[0292] Privacy and data security are paramount in the design of the Impulse Shopping feature. All user data, including shopping patterns and physiological markers, is securely encrypted and stored, with users having full control over their privacy settings. This ensures that users can confidently use the application without concerns about unauthorized access or misuse of their personal information.

[0293] The Impulse Shopping feature represents a significant advancement in the field of behavioral economics and mental well-being technology. By integrating with popular online stores and correlating shopping patterns with stress levels, the application provides real-time feedback and alternative suggestions to mitigate impulse shopping tendencies. This feature not only helps users manage their stress more effectively but also promotes healthier financial habits, contributing to overall well-being. The combination of real-time alerts, personalized insights, and social connectivity empowers users to develop better coping mechanisms and make more informed decisions about their shopping behaviors.

[0294] Binge Watching Detection: the present example implementation introduces a novel Binge Watching Detection feature within the mobile application, designed to identify and mitigate excessive television and streaming consumption by correlating viewing patterns with the user's emotional responses. This feature seamlessly integrates with popular streaming services such as Netflix, Apple TV, Amazon Video, and Hulu, providing users with real-time feedback and alternative suggestions to promote healthier entertainment habits and stress-relief activities.

[0295] Binge-watching, while sometimes a harmless leisure activity, can often be a coping mechanism for underlying stress or emotional distress. The application continuously monitors the user's interactions with streaming services, tracking metrics such as viewing duration,frequency, and the types of content consumed. By analyzing these patterm;, the application can detect potential binge-watching behaviors, particularly those that deviate from the user's ty pical viewing habits or coincide with periods of heightened stress.

[0296] To achieve this, the application utilizes advanced machine learning algorithms that analyze both historical and real-time data. These algorithms can identify spikes in viewing activity that align with periods of increased stress, as measured by wearable devices that monitor physiological markers such as heart rate variability (HRV) and electrodermal activity (EDA). When a correlation is detected, the application generates alerts to notify the user of their potential binge-watching tendencies.

[0297] When an alert is triggered, the application provides real-time feedback to the user, encouraging them to reconsider their viewing decisions. This feedback includes reminders of their stress levels and suggestions for alternative activities that can help alleviate stress more effectively. For instance, the application might suggest engaging in physical exercise, practicing mindfulness techniques, or participating in a social activity. By redirecting the user's focus away from prolonged screen time and towards healthier stress-relief activities, the application helps users develop better coping mechanisms and maintain a balanced lifestyle.

[0298] The Binge Watching Detection feature also includes a customizable setting that allows users to set limits on their streaming activities. Users can define specific parameters, such as a maximum number of viewing hours per day or week, and receive alerts when they approach these limits. This proactive approach empowers users to maintain control over their viewing habits and reinforces positive behavior changes.

[0299] In addition to real-time alerts, the application provides users with detailed insights into their viewing behaviors and stress levels over time. These insights are presented through intuitive visualizations that highlight patterns and trends, helping users understand the relationship between their emotional states and entertainment consumption. By identifying triggers for binge-watching, users can take preventive measures to manage their stress more effectively.

[0300] The application also supports social connectivity features, allowing users to share their progress with friends and family members. For example, users can choose to notify trusted contacts when they receive binge-watching alerts, fostering a supportive environment that encourages accountability and positive reinforcement. Friends and family can provide encouragement and suggestions for alternative stress-relief activities, further enhancing the user's ability to manage their viewing habits.

[0301] Privacy and data security are paramount in the design of the Binge Watching Detection feature. All user data, including viewing patterns and physiological markers, is securely encrypted and stored, with users having full control over their privacy settings. This ensures that users can confidently use the application without concerns about unauthorized access or misuse of their personal information.

[0302] The Binge Watching Detection feature also includes the ability to categorize content based on its potential impact on the user's emotional state. For instance, the application can differentiate between light-hearted comedies, intense dramas, and thrilling action movies. By understanding the emotional impact of different types of content, the application can make more tailored recommendations, suggesting lighter content during periods of high stress or advising a break from intense viewing sessions.

[0303] The Binge Watching Detection feature represents a significant advancement in the field of behavioral health and entertainment technology. By integrating with popular streaming sendees and correlating viewing patterns with stress levels, the application provides real-time feedback and alternative suggestions to mitigate excessive viewing tendencies. This feature not only helps users manage their stress more effectively but also promotes healthier entertainment habits, contributing to overall well-being. The combination of real-time alerts, personalized insights, and social connectivity empowers users to develop better coping mechanisms and make more informed decisions about their entertainment consumption.

[0304] Referring to FIGS. 1-2, an EEG electrode assembly may comprise basic components for acquiring bioelectric signals from a user. The assembly includes an EEG electrode 1 and a connecting wire 2 that work together to detect and transmit brainwave signals.

[0305] The EEG electrode 1 may be configured as a circular component designed to maintain contact with the skin near the mastoid bone or other temporal regions of the head. In some cases, the EEG electrode 1 comprises an active electrode positioned to contact skin behind the user's ear on a mastoid bone area. The EEG electrode 1 may be constructed from various materials and configurations depending on the specific application requirements.

[0306] The EEG electrode 1 may be implemented using different electrode types including dry electrodes, wet electrodes, gel electrodes, foam electrodes, gold-plated electrodes, silver / silver chloride (Ag / AgCl) electrodes, stainless steel electrodes, carbon nanotube electrodes, conductive polymer electrodes, textile electrodes (textrodes), disposable electrodes, reusable electrodes, flexible electrodes, rigid electrodes, or adhesive electrodes. In some cases, the EEG electrode 1 may be formed in various shapes including circular, rectangular, square,oval, triangular, hexagonal, elliptical, star-shaped, teardrop, crescent, diamond, trapezoidal, semi-circular, dome-shaped, concave, or convex configurations.

[0307] In some implementations, the EEG electrode 1 may include a spring-loaded system to push the electrode against the mastoid bone or scalp to improve contact and signal quality. The EEG electrode 1 may be filled with soft material including silicone gel, memory foam, medical-grade silicone, thermoplastic elastomers (TPE), polyurethane foam, conductive foam, hydrogel, soft rubber, viscoelastic foam, elastomeric gel, soft silicone rubber, gel pads, silicone rubber foam, conductive silicone, or soft polymer gel to enhance skin contact and user comfort.

[0308] In some cases, the EEG electrode 1 may be capable of transmitting EMF through the scalp and implementing transcranial stimulation including transcranial magnetic stimulation (TMS), transcranial electric stimulation (TES), Transcranial Direct Current Stimulation (tDCS), High-Definition Transcranial Direct Current Stimulation (HD-tDCS), Transcranial Alternating Current Stimulation (tACS), Transcranial Random Noise Stimulation (tRNS), Electroconvulsive Therapy (ECT), or Deep Brain Stimulation (DBS).

[0309] The connecting wire 2 extends from the EEG electrode 1 and provides electrical connectivity between the electrode and associated monitoring equipment. The connecting wire 2 features a flexible design that allows for movement while maintaining electrical continuity. In some cases, the connecting wire 2 may utilize different attachment methods including snap- fit, adhesive bonding, magnetic attachment, screw fastening, clip-on mechanism, press-fit, hinge connection, interlocking tabs, sliding mechanism, latch system, velcro straps, hook-and- loop fasteners, elastic bands, or wire hooks.

[0310] In some implementations, a plurality of EEG electrodes may be used including an active electrode, a reference electrode, and a ground electrode. The active electrode may be positioned near the mastoid bone, while the reference electrode may be integrated into an ear cuff structure. The ground electrode may be electrically coupled to a ground of a printed circuit board within a wireless controller. In such configurations, the reference electrode may be integrated into the ear cuff structure to provide a baseline measurement point, and the ground electrode may be electrically coupled to a ground of a printed circuit board within the wireless controller to establish a common electrical reference.

[0311] Referring to FIGS. 3-5, an ear cuff wearable device may be configured to be worn around a user's ear. The ear cuff wearable device features an ergonomic curved structure that conforms to the natural contours of the ear, allowing the device to wrap around the auricle. The curved structure may be designed to follow the anatomical shape of the ear, providing a secure and stable fit during extended wear periods.

[0312] The ear cuff wearable device may exhibit a sleek, streamlined design with a metallic finish that provides both aesthetic appeal and functional durability. The metallic construction may provide electromagnetic shielding for sensitive electronic components while offering resistance to daily wear and environmental factors. In some cases, the metallic finish may be constructed from materials including stainless steel, titanium, aluminum, gold-plated metal, silver-plated metal, copper, nickel-free alloys, gold, silver, or platinum.

[0313] The ear cuff may comprise a main body section that houses electronic components and sensors within the curved structure. The main body section may maintain a compact profile while accommodating the electronic circuitry needed for signal acquisition and wireless communication. In some implementations, the ear cuff may include an extension arm that extends from the main body, terminating at an electrode contact point positioned to make contact with the skin behind the ear.

[0314] The ear cuff may be detachably connected to an external case housing a wireless controller and wireless communication module. The external case may be positioned behind the user's ear when the device is worn, providing a discrete placement that minimizes visibility while maintaining functionality. In some cases, the detachable connection between the ear cuff and external case may allow users to have multiple ear cuffs that can be used interchangeably with the same external case to provide variety in style options.

[0315] The ear cuff may be secured to the user's ear using one or more attachment mechanisms to ensure stable electrode-skin contact during daily activities. The attachment means may be selected from the group consisting of adhesive stickers, rubber suction cups, magnetic attachment, silicone straps, clip-on mechanisms, and combinations thereof. In some cases, medical-grade adhesive may be used to provide hypoallergenic attachment that prevents skin irritation during extended wear. Magnetic attachment mechanisms may utilize thin magnetic strips paired with corresponding magnetic pads placed on the skin. Adjustable silicone straps may be designed to wrap around the ear and accommodate different ear sizes and shapes.

[0316] The device may be configured to be worn discreetly and comfortably throughout the day. The ergonomic design may distribute contact pressure evenly across the wearing surface, reducing discomfort during extended use periods. The curved geometry may follow the natural shape of the ear anatomy, allowing the device to maintain consistent contact with the skin while remaining stable during various activities.

[0317] The ear cuff wearable device may be capable of continuously acquiring EEG signals and transmitting the signals wirelessly in real time for external processing or display. Thecompact form factor may enable discreet placement around the ear while maintaining stability during movement and daily activities. In some implementations, the smooth, continuous surface of the device may suggest seamless integration of internal components within the housing, contributing to both aesthetic appeal and functional performance.

[0318] Referring to FIGS. 6-8, an ear cuff wearable device may be configured with a controller housing that accommodates electronic components for signal processing and wireless communication. The ear cuff may comprise a curved, hook-like structure that conforms to the natural contours of the ear, with a main body that wraps around the auricle of the ear.

[0319] The controller housing may be positioned at one end of the curved structure and may contain electronic components and processing circuitry. The controller housing may have a compact, rounded form factor that maintains aesthetic appeal while accommodating hardware components. In some cases, the controller housing may be designed to balance functionality with aesthetics, incorporating smooth curves and a minimalist appearance that allows the device to be worn discreetly.

[0320] The controller housing may include a wireless communication module configured to transmit digitized EEG data to an external device. The wireless communication module may comprise a Bluetooth Low Energy (BLE) controller that enables data transmission to paired devices. In some implementations, the controller may incorporate different wireless transceivers including Wi-Fi, Bluetooth, NFC, Zigbee, Z-Wave, RFID, Cellular networks such as 2GGSM, 3GWCDMA, HSDPA, 4GLTE, 5GNR, Satellite networks, DSL, Cable modem, Fiber optic, Tl / El, T3 / E3, SONET / SDH, ISDN, or Ethernet.

[0321] The BLE controller may include a signal conditioning circuit comprising at least one analog front-end (AFE) filter and a high-resolution analog-to-digital converter (ADC) configured to reduce electrical noise and improve EEG signal fidelity for downstream processing. The analog front-end may include different AFE components such as TSV521, OA4ZHA, TSZ124, MCP6L01, OPA4374, or MCP6L02. The signal conditioning circuit may incorporate analog filters including low pass, high pass, and notch filters to filter frequencies such as 50Hz or 60Hz to limit interference from external electronic devices.

[0322] The controller may operate at different sample rates including 256Hz or 512Hz and may comprise analog-to-digital converters with different bit resolutions including 12-bit, 16- bit, or 24-bit configurations. In some cases, the controller may include different System on Chip (SoC) modules from manufacturers such as Nordic, Texas Instruments, or ST Microelectronics.

[0323] The controller housing may incorporate different clock generator types including Atomic clock, Crystal oscillator, Rubidium clock, GPS disciplined clock, Oven-controlled crystal oscillator (OCXO), Temperature compensated crystal oscillator (TCXO), Voltage- controlled crystal oscillator (VCXO), Phase-locked loop (PLL) based clock, or MEMS-based clock. The clock generator may provide clock reference to the processor or other components of the board.

[0324] The controller may include different battery capacities such as 10mAh, 16mAh, or 100mAh at 3.7V, with battery life ranging from a few hours to over 40 days depending on battery capacity and algorithm optimization. The controller may support different charging connections including USB charging, contact charging, wireless charging, magnetic charging, solar charging, kinetic charging, battery swapping, contact-based charging, inductive charging, fast charging, trickle charging, cradle / dock charging, Qi standard charging, proprietary charging ports, portable power bank charging, or charging via computer interface.

[0325] An extension element may extend from the main body of the ear cuff, terminating at an electrode contact point. The extension element may be designed to position the electrode in contact with the skin behind the ear, targeting the mastoid bone area for signal acquisition. The extension element may allow for positioning adjustment to ensure proper skin contact for EEG signal detection while maintaining the overall structural integrity of the device.

[0326] The device may incorporate wireless connectivity capabilities, as evidenced by the standalone design without visible cables or external connections. The form factor may suggest integration of battery components within the controller housing, enabling portable operation for continuous brain activity monitoring. The overall design may demonstrate a balance between functionality and aesthetics, with the ear cuff structure providing stability and secure positioning while maintaining user comfort during extended wear periods.

[0327] Referring to FIGS. 9-11, alternative configurations of the ear cuff wearable device may provide different structural approaches for user comfort and signal acquisition. The ear cuff may be implemented with various design variations that accommodate different user preferences and anatomical requirements while maintaining functionality for EEG monitoring.

[0328] The ear cuff may feature an ergonomic curved structure designed to wrap around the auricle of the ear. The curved structure may include a main body portion that follows the natural contours of the ear, providing a secure and comfortable fit during extended wear periods. In some cases, the main body portion may extend from the upper portion of the ear down toward the earlobe area, maintaining contact with the skin for signal acquisition.

[0329] The ear cuff may incorporate a sleek, streamlined design with a metallic finish constructed from hypoallergenic materials. The curved structure may extend along different portions of the ear anatomy, with some configurations providing more extensive contact areas while others focus on specific anatomical regions. The design may emphasize both aesthetic appeal and functional performance, with smooth surfaces and rounded edges that minimize discomfort during daily use.

[0330] In some implementations, the ear cuff may demonstrate a minimalist approach to wearable technology, blending with the user's appearance while maintaining functionality for EEG monitoring. The compact form factor may allow for discreet wear throughout various daily activities without drawing attention to the device. The curved geometry may distribute contact pressure evenly across multiple contact points, reducing localized pressure that could cause discomfort during extended wear periods.

[0331] The ear cuff may be constructed with integrated electronic components housed within the curved structure. In some cases, the electronic components may be distributed throughout the ear cuff structure rather than concentrated in a single housing unit. The integration of components within the curved structure may provide a more balanced weight distribution and improved comfort compared to configurations with external controller housings.

[0332] Different ear cuff configurations may accommodate various electrode placement strategies. Some configurations may position electrodes at multiple contact points around the ear anatomy, while others may focus electrode placement on specific regions such as the temporal area or mastoid bone. The electrode positioning may be optimized based on the specific curved structure of each ear cuff configuration.

[0333] The ear cuff may incorporate different attachment mechanisms depending on the specific configuration. Some configurations may rely primarily on the curved structure's mechanical fit around the ear, while others may incorporate additional attachment means such as adhesive elements or magnetic connections. The attachment approach may be selected based on the intended use duration and activity level of the user.

[0334] In some cases, the ear cuff may be designed with adjustable curvature to accommodate different ear sizes and shapes. The adjustable design may allow users to customize the fit for optimal comfort and signal acquisition. The curved structure may incorporate flexible materials that can adapt to individual ear anatomy while maintaining structural integrity for reliable electrode contact.

[0335] The ear cuff configurations may vary in their coverage area around the ear. Some configurations may provide partial coverage focusing on specific anatomical regions, while others may offer more comprehensive coverage around the entire auricle. The coverage area may be selected based on the intended monitoring requirements and user comfort preferences.

[0336] Different configurations may incorporate varying degrees of flexibility in the curved structure. Some ear cuff designs may utilize more rigid materials for stable electrode positioning, while others may employ flexible materials that conform more closely to individual ear anatomy. The material selection may balance the competing requirements of structural stability and user comfort.

[0337] The ear cuff may be implemented with different thickness profiles depending on the configuration. Some designs may utilize thinner profiles for enhanced discreteness, while others may incorporate thicker sections to accommodate additional electronic components or battery capacity. The thickness profile may be optimized based on the specific functional requirements and aesthetic preferences for each configuration.

[0338] Referring to FIGS. 12-14, a block diagram illustrates the electronic architecture of an EEG wearable device system. The system may comprise multiple interconnected components that work together to acquire, process, and transmit brainwave signals for continuous monitoring applications.

[0339] The system may include a USB-C connector that serves as an interface for charging and data transfer operations. The USB-C connector may provide a standardized connection method that enables compatibility with various charging devices and computer interfaces. In some cases, the USB-C connector may support different charging protocols and data transfer rates depending on the specific implementation requirements.

[0340] A Li-ion charger may be operatively connected to the USB-C connector and configured to manage the charging process for the power storage components. The Li-ion charger may incorporate charging control circuitry that regulates voltage and current levels during the charging cycle. In some implementations, the Li-ion charger may include safety features such as overcharge protection, temperature monitoring, and current limiting to ensure safe operation during charging operations.

[0341] A Li-ion battery may be coupled to the Li-ion charger and configured to provide electrical power storage for the system. The Li-ion battery may be implemented as a rechargeable battery configured to provide electrical power to EEG electrodes and wireless communication components. In some cases, the Li-ion battery may have different capacity ratings such as 10mAh, 16mAh, or 100mAh at 3.7V, with operational duration ranging fromseveral hours to over 40 days depending on battery capacity and power optimization algorithms.

[0342] The system may include a rechargeable battery and power management system integrated within an ear cuff or associated housing. The power management system may comprise an LDO regulator that provides stable voltage regulation from the battery to power system components. The LDO regulator may be configured to maintain consistent voltage levels despite variations in battery charge levels or load conditions. In some implementations, the LDO regulator may provide multiple voltage rails to support different components with varying power requirements.

[0343] A power button may be incorporated into the system to control device operation and power states. The power button may enable users to turn the device on and off, initiate pairing modes, or activate specific functions such as starting or stopping EEG recording. In some cases, the power button may support multiple input methods such as single press, long press, or multiple press sequences to access different operational modes.

[0344] A status LED may be included to provide visual feedback regarding system operation and status. The status LED may communicate information including Bluetooth pairing status, successful connection establishment, battery charge levels, charging status, activity alerts, operational mode indications, and error alerts. In some implementations, the status LED may utilize different colors, blinking patterns, or brightness levels to convey different types of status information to the user.

[0345] The system may incorporate a System on Chip (SoC) that serves as the central processing unit for managing data processing and wireless communication operations. The SoC may include integrated antenna connections and GPIO interfaces for connecting to other system components. In some cases, the SoC may be implemented using modules from manufacturers such as Nordic, Texas Instruments, or ST Microelectronics, depending on the specific performance and feature requirements.

[0346] A signal acquisition module may comprise an analog front-end and analog-to- digital converter configured to detect and digitize brainwave signals including delta, theta, alpha, beta, and gamma waves. The analog front-end may be operatively connected to the SoC and configured to handle signal conditioning and processing operations. The analog front-end may perform 2: 1 lead measurement, indicating a two-electrode configuration for EEG signal acquisition.

[0347] The analog front-end may include different AFE components such as TSV521, OA4ZHA, TSZ124, MCP6L01, OPA4374, or MCP6L02. In some implementations, the analogfront-end may comprise analog filters including low pass, high pass, and notch filters configured to filter specific frequencies such as 50Hz or 60Hz to limit interference from external electronic devices.

[0348] The SoC may operate at different sample rates including 256Hz or 512Hz and may comprise analog-to-digital converters with different bit resolutions including 12-bit, 16-bit, or 24-bit configurations. The analog-to-digital converter may be configured to convert analog EEG signals into digital format for processing and transmission. In some cases, the system may transmit EEG data and brainwave data periodically to limit power consumption by the SoC.

[0349] The SoC may coordinate overall system operation, processing EEG signals received from the analog front-end and managing wireless data transmission through antenna interfaces. The SoC may include wireless transceivers supporting different communication protocols including Wi-Fi, Bluetooth, NFC, Zigbee, Z-Wave, RFID, Cellular networks such as 2G GSM, 3G WCDMA, HSDPA, 4G LTE, 5GNR, Satellite networks, DSL, Cable modem, Fiber optic, Tl / El, T3 / E3, SONET / SDH, ISDN, or Ethernet.

[0350] The system may include charging means selected from the group consisting of USB charging, contact charging, wireless charging, and magnetic charging. Contact charging may be implemented using metal contact points positioned on the device housing that interface with corresponding contacts on a charging station. Wireless charging may utilize inductive charging principles to transfer power without physical connections. Magnetic charging may employ magnetic connectors that automatically align and secure the charging interface.

[0351] Power distribution may flow from the USB-C connector through the Li-ion charger to the battery, then through the LDO regulator to supply clean power to the SoC and analog front-end components. The power management system may monitor battery levels and charging status, providing feedback through the status LED and managing power consumption to optimize operational duration.

[0352] A clock generator may provide timing reference signals to the processor and other components of the system. The clock generator may be implemented using different clock types including Atomic clock, Crystal oscillator. Rubidium clock, GPS disciplined clock, Oven-controlled crystal oscillator (OCXO), Temperature compensated crystal oscillator (TCXO), Voltage-controlled crystal oscillator (VCXO), Phase-locked loop (PLL) based clock, or MEMS-based clock configurations.

[0353] Referring to FIGS. 15-17, an ear cuff wearable device may be configured with a flexible connecting element that provides positioning adjustment capabilities between the main body and EEG electrode components. The ear cuff may feature a curved structure designed towrap around the auricle of the user's ear while incorporating a connecting element that allows for electrode positioning optimization.

[0354] The ear cuff may include a controller module positioned at one end of the curved structure, which may house electronic circuitry and wireless communication components. The controller module may be configured to process EEG signals and manage data transmission to external devices. In some cases, the controller module may be positioned behind the ear when the device is worn, providing discrete placement while maintaining functionality.

[0355] An EEG electrode may extend from the main body via a flexible connecting element that allows the electrode to be positioned against the skin for signal acquisition. The flexible connecting element may provide mechanical flexibility while maintaining electrical connectivity between the electrode and the controller module. In some implementations, the connecting element may be configured to position the electrode against the mastoid bone area behind the ear for signal detection.

[0356] The flexible connecting element may allow for positioning adjustment to ensure proper skin contact for EEG signal detection. The connecting element may be designed to accommodate different ear sizes and anatomical variations while maintaining consistent electrode contact pressure. In some cases, the connecting element may incorporate materials that provide both flexibility for positioning adjustment and structural stability for reliable electrode contact.

[0357] The curved design of the ear cuff may provide stability during wear while maintaining an aesthetically pleasing appearance. The ear cuff structure may be constructed from materials that can adapt to different ear sizes and shapes while maintaining the positioning of the connecting element. In some implementations, the ear cuff may be designed to distribute contact pressure evenly across multiple contact points to enhance user comfort during extended wear periods.

[0358] The connecting element may be configured to maintain electrical continuity between the electrode and the controller module throughout the range of positioning adjustments. The electrical connection may be maintained through flexible conductors that can accommodate the mechanical movement of the connecting element. In some cases, the connecting element may incorporate strain relief features to prevent damage to electrical connections during positioning adjustments or daily use.

[0359] The device may demonstrate a compact form factor that integrates EEG sensing capabilities into a wearable accessory format. The wireless controller module may enable data transmission to external devices while the electrode positioning allows for continuousmonitoring of brain activity. In some implementations, the connecting element may be designed to provide sufficient reach to position the electrode at optimal locations for signal acquisition while maintaining the overall structural integrity of the device.

[0360] The flexible connecting element may be constructed from materials that provide biocompatibility for skin contact applications. The materials may be selected to minimize skin irritation during extended wear while providing the mechanical properties needed for positioning adjustment. In some cases, the connecting element may incorporate hypoallergenic materials that are suitable for prolonged contact with skin surfaces.

[0361] The positioning adjustment capabilities of the connecting element may allow users to optimize electrode placement based on individual anatomical requirements. The adjustment range may accommodate variations in ear size, shape, and positioning preferences while maintaining effective signal acquisition. In some implementations, the connecting element may provide sufficient flexibility to accommodate head movement and daily activities without compromising electrode contact or signal quality.

[0362] The ear cuff configuration may balance functionality with wearability by providing a discreet solution for EEG monitoring applications. The flexible connecting element may enable the device to adapt to different use scenarios while maintaining consistent performance characteristics. In some cases, the connecting element may be designed to provide tactile feedback to users regarding proper electrode positioning and contact pressure.

[0363] Referring to FIGS. 18-20, multiple ear cuff device configurations may be implemented to provide different attachment methods and positioning approaches for EEG monitoring applications. The configurations may demonstrate various approaches to wireless connectivity and skin contact mechanisms that accommodate different user preferences and anatomical requirements.

[0364] The ear cuff configurations may utilize different attachment mechanisms to secure the device to the user's ear during operation. Some configurations may employ adhesive-based attachment methods that provide direct contact between the device components and the skin surface. The adhesive attachment may utilize medical-grade adhesive materials that maintain secure positioning while minimizing skin irritation during extended wear periods.

[0365] Alternative configurations may incorporate mechanical attachment approaches that rely on the structural design of the ear cuff to maintain positioning around the ear anatomy. The mechanical attachment may utilize the natural contours of the ear to provide stability without requiring adhesive materials. In some cases, the mechanical attachment may be combined with other securing methods to enhance stability during various activities.

[0366] The ear cuff configurations may vary in their approach to positioning the main device components relative to the ear anatomy. Some configurations may position the primary housing components behind the ear, providing discrete placement while maintaining access to the temporal region for signal acquisition. The behind-ear positioning may minimize the visual profile of the device while ensuring proper electrode contact with the skin surface.

[0367] Different configurations may incorporate varying approaches to electrode placement and skin contact mechanisms. Some configurations may utilize direct contact between integrated electrodes and the skin surface, while others may employ extending elements that position electrodes at specific anatomical locations. The electrode positioning may be optimized based on the intended monitoring requirements and the specific ear cuff configuration.

[0368] The wireless connectivity capabilities may be integrated differently across the various ear cuff configurations. Some configurations may house the wireless communication components within the main ear cuff structure, while others may position the wireless components in separate housing units that connect to the ear cuff. The wireless component placement may affect the overall form factor and weight distribution of the device.

[0369] The skin contact mechanisms may vary between configurations to accommodate different user comfort preferences and signal acquisition requirements. Some configurations may rely on continuous contact along the ear cuff structure, while others may focus contact pressure at specific electrode locations. The contact mechanism may be designed to maintain consistent signal acquisition while minimizing discomfort during extended wear.

[0370] Different ear cuff configurations may accommodate various ear sizes and shapes through adjustable design features. Some configurations may incorporate flexible materials that conform to individual ear anatomy, while others may utilize adjustable mechanical components that can be customized for different users. The adjustability features may enable optimal positioning for both comfort and signal quality.

[0371] The attachment methods may be selected based on the intended use duration and activity level of the user. Some configurations may be designed for temporary use with easily removable attachment mechanisms, while others may be optimized for extended wear with more secure attachment approaches. The attachment method selection may balance the competing requirements of security and ease of removal.

[0372] The positioning approaches may vary in their coverage area around the ear anatomy.Some configurations may provide focused contact at specific anatomical regions, while others may distribute contact across multiple areas of the ear. The coverage approach may be selectedbased on the specific monitoring requirements and the desired signal acquisition characteristics.

[0373] The wireless connectivity may be implemented using different communication protocols depending on the specific configuration requirements. Some configurations may utilize Bluetooth Low Energy for power-efficient communication, while others may incorporate alternative wireless protocols based on range and data transmission requirements. The wireless implementation may be optimized for the specific form factor and power constraints of each configuration.

[0374] The skin contact mechanisms may incorporate different materials and surface treatments to enhance biocompatibility and signal acquisition performance. Some configurations may utilize conductive materials that provide direct electrical contact, while others may employ capacitive coupling approaches that do not require direct electrical contact with the skin. The contact mechanism selection may be based on the specific signal acquisition requirements and user comfort considerations.

[0375] Referring to FIGS. 21-22, an ear cuff wearable device may be configured with a curved extension that wraps around the ear to provide comprehensive bioelectric signal monitoring capabilities. The ear cuff may feature a streamlined construction that integrates sensing technology within an ergonomic form factor designed to conform to the natural anatomy of the ear.

[0376] The ear cuff may comprise a main body section that houses electronic components and processing circuitry within a compact housing. The main body section may be positioned at one end of the curved structure and may contain wireless communication modules, signal processing components, and power management systems. In some cases, the main body section may be designed with a rounded form factor that maintains aesthetic appeal while accommodating the electronic hardware components.

[0377] A curved extension may extend from the main body section and may be configured to wrap around the auricle of the ear. The curved extension may follow the natural contours of the ear anatomy, providing multiple contact points for bioelectric signal acquisition. In some implementations, the curved extension may be designed to maintain consistent contact with the skin surface throughout the length of the extension, enabling signal monitoring from multiple anatomical locations.

[0378] The curved extension may terminate at an electrode contact point positioned at the opposite end from the main body section. The electrode contact point may be configured to maintain contact with specific anatomical regions such as the temporal area or mastoid bonefor signal acquisition. In some cases, the electrode contact point may be designed to provide stable contact pressure against the skin surface to enhance signal quality and reduce motion artifacts.

[0379] The streamlined construction of the ear cuff may integrate bioelectric signal monitoring capabilities throughout the curved structure. The curved extension may incorporate conductive pathways that connect electrode contact points to the processing circuitry housed within the main body section. In some implementations, the conductive pathways may be embedded within the curved extension structure to maintain electrical connectivity while preserving the aesthetic appearance of the device.

[0380] The ear cuff may be configured to monitor bioelectric signals from multiple electrode positions distributed along the curved extension. The electrode positions may be strategically located to capture signals from different anatomical regions around the ear. In some cases, the electrode positions may include an active electrode mounted directly on the surface of the main body section and a reference electrode positioned at the opposite end of the curved extension.

[0381] The curved extension may be constructed from materials that provide both flexibility for conforming to ear anatomy and structural stability for maintaining electrode contact. The materials may be selected to provide biocompatibilily for extended skin contact while maintaining the mechanical properties needed for reliable signal acquisition. In some implementations, the curved extension may incorporate hypoallergenic materials that minimize skin irritation during prolonged wear periods.

[0382] The streamlined design may minimize the visual profile of the device while maintaining functionality for continuous bioelectric monitoring. The curved extension may be designed to blend with the natural contours of the ear, reducing the apparent size of the device when worn. In some cases, the streamlined construction may distribute the device weight evenly around the ear to enhance comfort during extended wear periods.

[0383] The bioelectric signal monitoring capabilities may be implemented through electrode configurations positioned at different locations along the curved extension. The electrode configurations may include active electrodes for signal acquisition and reference electrodes for baseline measurements. In some implementations, ground electrodes may be electrically coupled to the ground of printed circuit boards within the main body section to establish electrical reference points.

[0384] The curved extension may be designed to accommodate different ear sizes and shapes through flexible construction approaches. The extension may incorporate materials thatcan adapt to individual ear anatomy while maintaining consistent electrode contact pressure. In some cases, the curved extension may be configured to provide adjustable positioning for electrode contact points to optimize signal acquisition for different users.

[0385] The ear cuff may operate by maintaining consistent contact with the skin through the curved extension design, enabling continuous monitoring of bioelectric signals throughout daily activities. The curved extension may provide stability during movement while maintaining the electrical contact needed for signal acquisition. In some implementations, the curved extension may be designed to accommodate head movement and physical activities without compromising signal quality or device positioning.

[0386] The streamlined construction may integrate wireless communication capabilities within the main body section while distributing sensing capabilities throughout the curved extension. The wireless communication may enable real-time transmission of bioelectric signals to external devices for processing and analysis. In some cases, the streamlined design may incorporate antenna elements within the curved structure to enhance wireless communication performance while maintaining the aesthetic appearance of the device.

[0387] Referring to FIGS. 23-25, an ear cuff wearable device may be configured with a front portion that extends toward the tragus area of the ear. The ear cuff may comprise an ergonomic curved structure designed to wrap around the auricle of the ear while incorporating a front extension that provides secure attachment and positioning capabilities.

[0388] The front portion of the ear cuff may extend toward the tragus area to create a secure attachment point that utilizes the natural anatomy of the ear. The front extension may be positioned to make contact with the tragus region, providing a stable anchor point that helps maintain the overall positioning of the ear cuff during wear. In some cases, the front portion may be designed to conform to the contours of the tragus area, creating a custom fit that enhances stability and comfort.

[0389] The secure attachment mechanism may be implemented through the curved design that creates a continuous loop around portions of the ear anatomy. The front portion may work in conjunction with other sections of the ear cuff to distribute attachment forces across multiple contact points, reducing localized pressure while maintaining secure positioning. In some implementations, the attachment mechanism may rely on the mechanical fit between the ear cuff structure and the natural contours of the ear.

[0390] The ear cuff may maintain a low-profile form factor that minimizes the visual appearance of the device when worn. The front portion may be designed with a streamlined profile that follows the natural curves of the ear anatomy, reducing the apparent size of thedevice. In some cases, the low-profile design may enable the ear cuff to blend with the user's appearance, making the device less noticeable during daily activities.

[0391] The curved structure of the ear cuff may create a continuous path from the front portion to other sections of the device, maintaining structural integrity while providing flexibility for different ear sizes and shapes. The front portion may be constructed from materials that provide both the mechanical properties needed for secure attachment and the biocompatibility required for skin contact applications.

[0392] The front extension may be configured to provide tactile feedback to users regarding proper positioning and fit. The contact with the tragus area may serve as a reference point that helps users position the ear cuff correctly for optimal signal acquisition and comfort. In some implementations, the front portion may incorporate surface textures or contours that enhance the mechanical interface with the ear anatomy.

[0393] The secure attachment mechanism may accommodate different ear anatomies through the flexible design of the front portion. The extension toward the tragus area may be configured to adapt to variations in ear size and shape while maintaining consistent attachment performance. In some cases, the front portion may provide sufficient flexibility to accommodate individual anatomical differences without compromising the secure fit.

[0394] The low-profile form factor may be achieved through carefill design of the front portion thickness and curvature. The front extension may be configured with a minimal cross- sectional area that reduces the visual impact while maintaining the structural properties needed for secure attachment. In some implementations, the front portion may incorporate tapered edges that create a smooth transition between the device and the ear anatomy.

[0395] The front portion may be designed to work in coordination with other attachment mechanisms positioned at different locations around the ear cuff The tragus area contact may provide one component of a multi-point attachment system that distributes securing forces across the ear anatomy. In some cases, the front portion may serve as a primary attachment point while other sections of the ear cuff provide secondary support and positioning.

[0396] The ear cuff configuration may enable the front portion to maintain consistent contact with the tragus area throughout various head movements and daily activities. The curved design may provide sufficient flexibility to accommodate natural movement while maintaining the secure attachment provided by the front extension. In some implementations, the front portion may be configured to provide spring-like properties that maintain contact pressure despite movement or positioning changes.

[0397] Referring to FIGS. 26-28, multiple ear cuff components may be implemented in different configurations to provide design variations and modular attachment mechanisms that accommodate various ear sizes and shapes. The ear cuff components may demonstrate different structural characteristics and attachment approaches that enable customization for individual user requirements.

[0398] The ear cuff components may be arranged in multiple rows displaying various design variations and attachment mechanisms. The components may feature different curvature profiles, thickness dimensions, and overall form factors that accommodate different ear anatomies. In some cases, the components may- be designed with varying degrees of structural flexibility to conform to individual ear shapes while maintaining positioning stability.

[0399] The ear cuff components may exhibit different structural characteristics including variations in hook mechanisms and clasp configurations that secure the device to the ear anatomy. Some components may display angular design approaches with defined edges and geometric forms, while others may feature smoother, more organic curves that follow natural ear contours. The structural variations may provide different mechanical properties for attachment and positioning.

[0400] The modular nature of the ear cuff system may be demonstrated through components that can be configured to create various device arrangements. The components may be designed to accommodate different ear sizes through adjustable curvature and flexible attachment points. In some implementations, the components may provide interchangeable elements that allow users to select configurations based on comfort preferences and anatomical requirements.

[0401] The ear cuff components may vary in their approach to mechanical attachment and positioning around the ear anatomy. Some components may utilize more pronounced arc shapes that provide extensive contact with the ear surface, while others may present subtle curves that focus contact at specific anatomical points. The curvature variations may enable different approaches to weight distribution and contact pressure management.

[0402] The components may be constructed from materials that provide both flexibility for comfortable wear and structural stability for reliable positioning during use. The material selection may vary between components to provide different mechanical properties such as elasticity, durability, and biocompatibility. In some cases, the components may incorporate surface treatments or textures that enhance the mechanical interface with ear anatomy.

[0403] Different attachment mechanisms may be incorporated into the ear cuff components to provide secure positioning options. Some components may feature spring-loadedmechanisms that maintain contact pressure, while others may rely on mechanical fit between the component structure and ear contours. The attachment mechanisms may be selected based on the intended use duration and activity level requirements.

[0404] The ear cuff components may demonstrate different approaches to electrode positioning and signal acquisition contact points. Some components may incorporate integrated electrode surfaces within the curved structure, while others may provide mounting points for separate electrode elements. The electrode positioning approaches may be optimized based on the specific curvature and contact characteristics of each component.

[0405] The modular design approach may enable users to select ear cuff components based on aesthetic preferences and functional requirements. The components may be available in different colors, finishes, and surface treatments that provide customization options. In some implementations, the modular approach may allow users to interchange components without replacing the entire device system.

[0406] The ear cuff components may accommodate different ear sizes through scalable design features that maintain proportional relationships between component dimensions. The scalability may be achieved through parametric design approaches that adjust curvature, thickness, and attachment point spacing based on ear size measurements. In some cases, the components may provide size-specific variations that optimize fit for different user populations.

[0407] The attachment mechanisms may provide different levels of adjustability to accommodate individual anatomical variations. Some components may incorporate adjustable elements that can be customized during fitting, while others may rely on material flexibility to adapt to different ear shapes. The adjustability features may enable fine-tuning of contact pressure and positioning for optimal comfort and signal acquisition.

[0408] The ear cuff components may be designed to work in conjunction with external housing units that contain electronic components and processing circuitry. The modular approach may enable the ear cuff components to be detached from electronic housings for cleaning, replacement, or style changes. In some implementations, the modular design may allow multiple ear cuff components to be used with a single electronic housing unit.

[0409] The design variations may accommodate different aesthetic preferences through varying visual profiles and surface characteristics. Some components may feature minimalist designs with clean lines and simple curves, while others may incorporate more complex geometries with multiple contact points. The aesthetic variations may enable users to select components that complement their personal style preferences.

[0410] The ear cuff components may incorporate different approaches to weight distribution around the ear anatomy. Some components may distribute weight evenly along the entire curved structure, while others may concentrate weight at specific attachment points. The weight distribution approach may affect user comfort during extended wear periods and may be optimized based on the specific component configuration.

[0411] Referring to FIGS. 29-30, a headstrip 11 / 12 may be configured as an elongated structural element designed for EEG monitoring applications across various headwear configurations. The headstrip 11 / 12 may form a primary structural component that enables brain activity monitoring through strategic electrode positioning and modular attachment mechanisms.

[0412] The headstrip 11 / 12 may be designed to attach to different headwear including caps, hats, headbands, safety helmet, safety cap, motorcycle helmet, racing helmet, skiing helmet, climbing hat, or bicycle bump cap. The attachment capability may enable the headstrip 11 / 12 to be integrated with existing headwear without requiring specialized equipment or custom- fitted devices. In some cases, the headstrip 11 / 12 may be configured to conform to the interior surfaces of various headwear types while maintaining proper electrode positioning for signal acquisition.

[0413] The headstrip 11 / 12 may include multiple circular elements arranged in a linear configuration along the elongated structure. The circular elements may represent EEG electrode positions strategically located to capture brain activity from frontal regions of the skull. In some implementations, the electrode positions on the headstrip 11 / 12 may correspond to standard EEG electrode placement locations according to established measurement systems such as the 10-20 or 10-10 electrode positioning protocols.

[0414] The headstrip 11 / 12 may incorporate different adhesive materials on a back side to secure the assembly to headwear or directly to the user's forehead. The adhesive materials may include medical-grade adhesive, silicone adhesive, double-sided tape, hydrogel adhesive, conductive adhesive, pressure-sensitive adhesive, acrylic adhesive, epoxy adhesive, polyurethane adhesive, butyl rubber adhesive, thermoplastic adhesive, hot melt adhesive, adhesive gel pads, fabric adhesive, or velcro strips. The adhesive material selection may be based on the intended application duration, skin compatibility requirements, and attachment surface characteristics.

[0415] A controller 29 may be positioned centrally on the headstrip 11 / 12 and may comprise a rectangular component housing electronic circuitry for EEG signal processing operations. The controller 29 may be configured to collect brainwave data from the electrodepositions distributed along the headstrip 11 / 12. In some cases, the controller 29 may include wireless communication capabilities to transmit processed EEG signals to external devices for real-time analysis and feedback.

[0416] The confroller 29 may be operatively connected to the electrode positions on the headstrip 11 / 12 through conductive pathways integrated within the headstrip structure. The conductive pathways may enable the confroller 29 to acquire signals from multiple electrode locations simultaneously, providing comprehensive monitoring of brain activity across the frontal region. In some implementations, the controller 29 may include signal conditioning circuitry to filter and amplify EEG signals before wireless transmission.

[0417] An interface cradle 30 may be configured as a separate circular component that provides a mounting interface for the controller 29. The interface cradle 30 may enable the controller 29 to be detached from the headstrip 11 / 12 for charging operations or transfer between different headstrip units. In some cases, the interface cradle 30 may provide mechanical and electrical connections that allow the controller 29 to be removed while maintaining the positioning of the headstrip 11 / 12.

[0418] The interface cradle 30 may enable a removable controller configuration allowing one controller 29 to be dynamically attached to multiple hats with permanently attached cradles. The modular design approach may enable users to install interface cradles 30 on multiple headwear items while utilizing a single controller 29 that can be transferred between different headwear as needed. In some implementations, the interface cradle 30 may provide standardized mounting interfaces that ensure consistent electrical and mechanical connections regardless of the specific headwear configuration.

[0419] The modular design may enable the controller 29 to be removed from the headstrip 11 / 12 assembly for charging operations without requiring removal of the entire headstrip from the headwear. The interface cradle 30 may maintain the positioning and attachment of the headstrip 11 / 12 while allowing the controller 29 to be charged separately. In some cases, the interface cradle 30 may include electrical contacts that interface with corresponding contacts on the controller 29 to provide power and data connections.

[0420] The linear arrangement of electrode positions on the headstrip 11 / 12 may correspond to frontal EEG measurement locations that provide monitoring of brain activity associated with cognitive and emotional processes. The electrode positioning may be optimized to capture signals from regions of the brain involved in attention, working memory, and emotional regulation. In some implementations, the headstrip 11 / 12 may be configured tomonitor specific brainwave frequencies including delta, theta, alpha, beta, and gamma waves from the frontal cortex region.

[0421] The headstrip 11 / 12 may be constructed from flexible materials that conform to the curvature of headwear and the user's forehead while maintaining consistent electrode contact. The flexible construction may enable the headstrip 11 / 12 to adapt to different head sizes and headwear configurations without compromising signal acquisition performance. In some cases, the headstrip 11 / 12 may incorporate materials that provide both mechanical flexibility and electrical conductivity for electrode connections.

[0422] The interface cradle 30 may provide mechanical retention features that secure the controller 29 during use while enabling easy removal when needed. The retention features may include snap-fit mechanisms, magnetic connections, or mechanical latches that provide secure attachment without requiring tools for removal. In some implementations, the interface cradle 30 may include alignment features that ensure proper positioning of the controller 29 relative to the electrode connections on the headstrip 11 / 12.

[0423] Referring to Figure 31, a wearable EEG device may be positioned on a human ear to enable continuous monitoring of brain activity through strategic anatomical placement. The device positioning may utilize the natural contours and anatomical features of the ear to establish stable contact points for bioelectric signal acquisition.

[0424] The wearable EEG device may be positioned around a user's ear by conforming the ear cuff structure to the outer contour of the auricle. The ear cuff may wrap around the curved anatomy of the ear, extending from the upper portion of the ear structure down toward the earlobe region. In some cases, the ear cuff may maintain contact with the ear surface throughout the length of the curved structure, providing multiple potential contact points for signal monitoring.

[0425] The anatomical alignment of the wearable EEG device may be achieved through positioning the ear cuff to follow the natural shape of the auricle while maintaining consistent contact with the skin surface. The curved configuration may allow the device to conform to individual ear anatomy variations while maintaining proper positioning for signal acquisition. In some implementations, the ear cuff may be positioned to distribute contact pressure evenly across the ear surface, reducing localized pressure points that could cause discomfort during extended wear periods.

[0426] The signal acquisition locations may be established through positioning at least one EEG electrode at strategic anatomical points around the ear region. The electrode positioning may target the temporal region of the skull, including areas near the mastoid bone behind theear. In some cases, the electrode may be positioned to maintain contact with the mastoid bone area, which may provide access to brainwave signals from the temporal cortex region.

[0427] The contact points between the wearable EEG device and the user's ear may be established through the curved structure that maintains skin contact at multiple locations around the ear anatomy. The main body section of the ear cuff may provide contact along the outer rim of the ear, while extending elements may position electrodes at specific anatomical locations behind the ear. In some implementations, the contact points may be distributed to provide both mechanical stability for device positioning and electrical contact for signal acquisition.

[0428] The positioning of the wearable EEG device may enable detection of brainwave signals through electrode contact with skin surfaces that provide access to underlying neural activity. The electrode positioning behind the ear may target the temporal region where brainwave signals can be detected through the skull and scalp tissues. In some cases, the electrode contact may be maintained through consistent pressure against the skin surface to minimize signal artifacts and enhance signal quality.

[0429] The anatomical alignment may accommodate different ear sizes and shapes through the flexible design of the ear cuff structure. The curved configuration may adapt to individual anatomical variations while maintaining the positioning needed for effective signal acquisition. In some implementations, the ear cuff may provide sufficient flexibility to conform to different ear geometries without compromising the contact pressure needed for reliable electrode performance.

[0430] The signal acquisition locations may be optimized based on the accessibility of brainwave signals from different anatomical regions around the ear. The positioning behind the ear may provide access to temporal lobe activity, while contact points along the ear structure may enable monitoring of additional brain regions. In some cases, the electrode positioning may be selected to capture specific brainwave frequencies associated with cognitive and emotional processes.

[0431] The wearable EEG device positioning may maintain stability during head movement and daily activities through the secure fit provided by the ear cuff structure. The curved design may accommodate natural head movement while maintaining the electrode contact needed for continuous signal monitoring. In some implementations, the positioning may be designed to remain stable during various physical activities without requiring readjustment or repositioning.

[0432] The contact points may be established through materials and surface treatments that enhance the interface between the device and the skin surface. The electrode contact areas may utilize conductive materials that provide electrical connectivity for signal acquisition. In some cases, the contact points may incorporate surface textures or treatments that improve the mechanical and electrical interface with the skin while maintaining biocompatibility for extended wear applications.

[0433] Referring to FIGS. 32-33, a mobile application may be operatively connected to the ear cuff via wireless communication to provide comprehensive user interface capabilities for emotional state monitoring and data visualization. The mobile application may comprise interface components that enable both subjective user input and real-time display of brainwave- derived emotional state information.

[0434] The mobile application may be configured to receive transmitted brainwave signals from a wireless communication module and analyze the brainwave signals using machine learning algorithms to determine emotional states of a user. The mobile application may provide real-time feedback regarding the emotional states to the user through user interface components that facilitate both data input and visualization operations.

[0435] A self-report interface 32 may be configured to allow the user to input subjective emotional state data through interactive interface elements. The self-report interface 32 may be displayed on a mobile device screen and may include a greeting message positioned at the top of the interface. In some cases, the self-report interface 32 may present a question prompt such as "How are you feeling?" to guide user interaction with the interface components.

[0436] The self-report interface 32 may comprise an interactive slider with a color gradient scale ranging from calm to alert states. The interactive slider may be implemented as a semicircular control element positioned in the center of the interface screen. In some implementations, the color gradient may utilize different color combinations including blue shades for calm states, yellow shades for alert states, and white for neutral states. The color gradient may range from blue on the left side to orange on the right side of the slider control.

[0437] The self-report interface 32 may include a circular indicator lhat can be moved along the slider to select the user's current emotional state. The circular indicator may be configured as a draggable element that allows users to position the indicator at specific points along the color gradient scale. In some cases, the slider may provide selectable arousal values ranging from 1.0 to 10.0 while displaying seven points on a Likert scale including Very calm (Low Arousal), Calm, Slightly calm, Neutral, Slightly Alert, Alert, and Very alert (High Arousal).

[0438] The self-report interface 32 may provide dynamic text feedback that updates in realtime based on the user's slider position. The dynamic text feedback may be displayed below the slider control and may present text such as "I feel neutral" corresponding to the selected position on the slider. In some implementations, the text feedback may change automatically as the user moves the circular indicator along the slider scale, providing immediate confirmation of the selected emotional state.

[0439] The self-report interface 32 may include a submit button positioned at the bottom of the interface to record the user's input. The submit button may enable users to log their subjective emotional states for correlation with objective brainwave measurements. In some cases, the self-report interface 32 may include navigation icons displayed at the bottom of the screen to enable movement between different application sections.

[0440] A live-feed interface 33 may be configured to display real-time emotional state data derived from brainwave signals acquired by the EEG electrode. The live-feed interface 33 may provide objective feedback regarding the user's emotional states through real-time data visualization components. In some implementations, the live-feed interface 33 may display brain signals in seismograph format and brain map representations at different brainwave bands including delta, theta, alpha, beta, and gamma frequencies.

[0441] The live-feed interface 33 may include a connection status indicator positioned at the top of the interface to show the communication status between the wearable device and the mobile application. The connection status indicator may display text such as "Connected" along with accompanying visual indicators to confirm active data transmission. In some cases, the connection status may be represented through colored indicators that provide immediate visual feedback regarding communication status.

[0442] The live-feed interface 33 may display a horizontal waveform graph that presents real-time EEG signal data acquired from the EEG electrode. The waveform graph may be positioned below the connection status indicator and may provide continuous visualization of brainwave activity as the signals are being recorded. In some implementations, the waveform display may utilize seismograph-style visualization that presents neural activity in a format similar to seismic recording systems.

[0443] The live-feed interface 33 may include the same semi-circular slider design as the self-report interface 32, with the current emotional state indicated through automated positioning based on brainwave analysis. The slider may display real-time emotional state classifications such as "slightly alert" based on machine learning analysis of the acquired brainwave signals. In some cases, the live-feed interface 33 may provide vertical graphrepresentation showing time on a y-axis and arousal on an x-axis to eliminate bias associated with traditional top-down approaches.

[0444] The live-feed interface 33 may include a detailed history graph positioned in the lower portion of the interface screen. The history graph may show emotional state fluctuations over time with color-coded timeline representations. In some implementations, the history graph may utilize the same color gradient scheme as the slider controls, with blue representing calm states and orange representing alert states throughout the monitored time period.

[0445] The history graph may include time interval markings on a horizontal axis showing progression from "15 minutes ago" to "now" to enable users to track emotional state changes over recent time periods. The graph may use color gradients to represent different emotional states throughout the monitored duration, providing visual correlation between time periods and emotional state classifications.

[0446] The mobile application may be configured to display real-time emotional insights, log user input, and track emotional trends over time through the combined functionality of the self-report interface 32 and live-feed interface 33. The user interface components may enable correlation between subjective self-reported emotional state data and objective brainwave- derived emotional state measurements. In some cases, the data from the interactive slider may be used to refine machine learning model predictions of emotional state through comparison with objective brainwave measurements.

[0447] Referring to Figure 34, the interface cradle 30 may be implemented within a mobile application interface that provides comprehensive calendar view and data visualization components for tracking emotional states over extended time periods. The interface cradle 30 may serve as a foundational component that enables comprehensive tracking and analysis features through integration with calendar-based data visualization systems.

[0448] The mobile application interface may display a monthly calendar positioned at the top portion of the interface screen, with individual days represented as circular elements that may be color-coded to indicate different emotional states or activity levels throughout the monitored time period. The calendar display may utilize color-coding systems where each day may be represented by a single color that summarizes brain activity patterns for that specific day. In some cases, the color-coding may utilize blue shades to represent calm states, yellow shades to represent alert states, and white to indicate neutral states based on the emotional state data collected through the wearable EEG device.

[0449] The calendar interface may include navigation arrows positioned on either side of the month display to allow users to move between different months and access historicalemotional state data. The navigation functionality may enable users to review long-term patterns and trends in emotional states across multiple months of data collection. In some implementations, the calendar view may provide immediate visual summary capabilities that allow users to identify patterns and significant changes in emotional states over extended time periods.

[0450] A signal processing engine may be configured to receive EEG data and extract temporal and spectral features from the brainwave signals collected by the wearable EEG device. The signal processing engine may analyze the collected EEG data to identify patterns corresponding to different emotional states throughout each monitored day. In some cases, the signal processing engine may process brainwave signals in real time to generate the color- coded representations displayed in the calendar interface.

[0451] The mobile application may incorporate machine learning algorithms that comprise at least one classifier selected from the group consisting of Support Vector Machines, Random Forest, Neural Networks, K-Nearest Neighbors, and combinations thereof to analyze brainwave patterns and determine emotional states. The machine learning classifiers may be configured to map the brainwave signals to emotional states using Russell's circumplex model of emotions along axes of arousal and valence. In some implementations, the emotional states may be determined based on a combination of EEG-derived features or brainwave features calculated in real time from the acquired brainwave signals.

[0452] The interface may include a summary section positioned below the calendar display that shows quantitative data about the user's emotional states for a selected day. The summary section may display statistics such as "43 min Calm" and "31 min Alert" to provide detailed breakdowns of time spent in different emotional states throughout the selected day. In some cases, the summary data may be generated through analysis of brainwave signals using artificial intelligence algorithms that map the brainwave signals into emotional states based on the Russell's circumplex model.

[0453] A detailed graph may be positioned in the lower portion of the interface to display fluctuations in emotional states over a 24-hour period for the selected day. The detailed graph may show varying levels of emotional activity with peaks and valleys representing changes in the user's emotional state throughout the monitored time period. In some implementations, the horizontal axis of the graph may represent time progression while the vertical axis may indicate the intensity or level of the measured emotional states derived from the brainwave analysis.

[0454] An Al coach module may be configured to interpret the inferred emotional states displayed in the calendar and graph interfaces and deliver real-time, personalized feedback andself-regulation guidance via natural language interaction or visual prompts. The Al coach module may incorporate a reinforcement learning algorithm configured to adapt feedback based on the user's historical responses, emotional patterns, and behavioral trends identified through the calendar visualization system In some cases, the Al coach may integrate multimodal sensor data, including EEG, electrodermal activity (EDA), and photoplethysmography (PPG), to enhance the accuracy of emotional state classification and coaching recommendations.

[0455] The mobile application may provide real-time neurofeedback through audio, visual, or haptic cues triggered in response to detected stress, anxiety, or emotional dysregulation patterns identified through the calendar and graph analysis. The digital neuro-coach may be powered by artificial intelligence and configured to analyze the emotional states displayed in the interface and deliver personalized, real-time feedback and recommendations to the user to improve mental wellness. In some implementations, the digital neuro-coach may provide realtime nudges to encourage the user to engage in calming or stimulating activities based on the detected emotional state patterns shown in the calendar visualization.

[0456] The interface may function as a journal module within the application that visualizes historical emotional state data using color-coded graphs and time-based summaries to help identify behavioral patterns over extended monitoring periods. The journal functionality may enable users to correlate self-reported emotional state data collected through interactive interfaces with the analyzed brainwave signals to improve accuracy of emotional state determination. In some cases, the calendar and graph visualization may support the generation of customized recommendations for stress management activities based on identified patterns between self-reported emotional state data and the analyzed brainwave signals.

[0457] The navigation bar positioned at the bottom of the interface screen may contain multiple icons with highlighted indicators showing the current active section of the application. The navigation functionality may enable users to move between different interface components including self-report interfaces, live-feed interfaces, and the calendar-based visualization system In some implementations, the interface design may allow users to view both high-level monthly patterns through the calendar display and detailed daily breakdowns through the graph visualization, providing comprehensive access to emotional state data collected by the wearable EEG device system.

[0458] A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scopeof the disclosure. Accordingly, other implementations are within the scope of the following claims.

Claims

CLAIMSI / we claim:

1. A system for continuous monitoring of brain activity and real-time emotional coaching, comprising: an ear cuff wearable device configured to be worn around a user's ear, the device comprising:• one or more electroencephalography (EEG) electrodes positioned to maintain contact with the skin near the mastoid bone or other temporal regions of the head;• a signal acquisition module comprising an analog front-end and analog-to- digital converter to detect and digitize brainwave signals including delta, theta, alpha, beta, and gamma waves;• a wireless communication module configured to transmit digitized EEG data to an external device;• a rechargeable battery and power management system integrated within the ear cuff or its associated housing; a mobile application operatively connected to the ear cuff via wireless communication, the application comprising:• a signal processing engine configured to receive EEG data and extract temporal and spectral features;• a machine learning model trained to infer emotional states, including arousal and valence levels, from said EEG features;• an Al coach module configured to interpret the inferred emotional states and deliver real-time, personalized feedback and self-regulation guidance via natural language interaction or visual prompts; and• a user interface configured to display real-time emotional insights, log user input, and track emotional trends over time.

2. The system of claim 1, wherein the Al coach module further comprises a reinforcement learning algorithm configured to adapt its feedback based on the user's historical responses, emotional patterns, and behavioral trends.

3. The system of claim 1, wherein the user interface includes a dynamic emotional slider for manual self-reporting of mood and arousal levels, the data from which is used to refine the machine learning model’s predictions of emotional state.

4. The system of claim 1, wherein the mobile application provides real-time neurofeedback through audio, visual, or haptic cues triggered in response to detected stress, anxiety, or emotional dysregulation.

5. The system of claim 1, wherein the Al coach integrates multi-modal sensor data, including EEG, electrodermal activity (EDA), and photoplethysmography (PPG), to enhance the accuracy of emotional state classification and coaching recommendations.

6. A wearable electroencephalography (EEG) device for continuous monitoring of brain activity, comprising: an ear cuff structure ergonomically designed to fit around the auricle of a user’s ear; at least one EEG electrode positioned on the ear cuff and configured to contact the skin near the mastoid bone to detect brainwave signals; a Bluetooth Low Energy (BLE) controller mounted on or integrated within the ear cuff, configured to process and wirelessly transmit EEG signals acquired from the electrode; a power source coupled to the BLE controller and electrode, configured to provide power for signal acquisition and wireless transmission; wherein the device is configured to be worn discreetly and comfortably throughout the day, and is capable of continuously acquiring EEG signals and transmitting them wirelessly in real time for external processing or display.

7. The device of claim 6, further comprising a plurality of EEG electrodes including an active electrode, a reference electrode, and a ground electrode, wherein the activeelectrode is positioned near the mastoid bone, the reference electrode is integrated into the ear cuff, and the ground electrode is electrically coupled to the ground of the printed circuit board inside the device.

8. The device of claim 6, wherein the ear cuff is secured to the user's ear using one or more of the following attachment mechanisms: medical-grade adhesive, magnetic attachment, clip-on mechanism, or adjustable silicone strap, to ensure stable electrode-skin contact during daily activities.

9. The device of claim 6, wherein the Bluetooth Low Energy (BLE) controller includes a signal conditioning circuit comprising at least one analog front-end (AFE) filter and a high-resolution analog-to-digital converter (ADC) configured to reduce electrical noise and improve EEG signal fidelity for downstream processing.

10. The device of claim 6, further comprising an artificial intelligence (Al) module integrated with a mobile application, wherein the Al module receives the EEG signals transmitted by the device and is configured to analyze brainwave patterns to determine the user's emotional state and provide real-time personalized feedback through an Al-based digital coach.

11. A system for enhancing mental well-being using brainwave-based emotional analysis, comprising: a wearable electroencephalography (EEG) device configured to continuously acquire brainwave signals from a user; a mobile application operatively coupled to the EEG device, the application comprising:• a signal processing module configured to receive and filter EEG signals in real time;• a machine learning module configured to map the processed EEG signals into emotional states using at least one classifier;• a digital neuro-coach powered by artificial intelligence, configured to analyze the emotional states and deliver personalized, real-time feedback and recommendations to the user to improve mental wellness;wherein the emotional states are mapped along axes of arousal and valence based on a psychological model, and the feedback includes mood tracking, proactive nudges, and self-awareness tools displayed via a user interface on the mobile application.

12. The system of claim 11, wherein the digital neuro-coach provides real-time nudges to encourage the user to engage in calming or stimulating activities based on the detected emotional state.

13. The system of claim 11, wherein the mobile application includes a self-reporting module comprising an interactive slider and dynamic text feedback that allows the user to log subjective emotional states.

14. The system of claim 11, further comprising a journal module within the application that visualizes historical emotional state data using color-coded graphs and time-based summaries to help identify behavioral patterns.

15. The system of claim 11, wherein the emotional states are determined based on a combination of EEG-derived features or brainwave features calculated in real time.

16. A wearable electroencephalography (EEG) device, comprising: an ear cuff configured to be positioned around an auricle of a user's ear, the ear cuff comprising a curved structure that conforms to natural contours of the ear; at least one EEG electrode positioned on the ear cuff and configured to contact skin for detecting brainwave signals; a wireless controller integrated with the ear cuff and configured to process the brainwave signals from the at least one EEG electrode; and a wireless communication module configured to transmit the processed brainwave signals to an external device.

17. The device of claim 16, wherein the at least one EEG electrode comprises an active electrode positioned to contact skin behind the user's ear on a mastoid bone area.

18. The device of claim 17, further comprising a reference electrode integrated into the ear cuff structure.

19. The device of claim 18, further comprising a ground electrode electrically coupled to a ground of a printed circuit board within the wireless controller.

20. The device of claim 16, wherein the wireless communication module comprises a Bluetooth Low Energy (BLE) controller.

21. The device of claim 16, further comprising a rechargeable battery configured to provide electrical power to the at least one EEG electrode and the wireless communication module.

22. The device of claim 21, further comprising charging means selected from the group consisting of USB charging, contact charging, wireless charging, and magnetic charging.

23. The device of claim 16, wherein the ear cuff is detachably connected to an external case housing the wireless controller and wireless communication module.

24. The device of claim 23, wherein the external case is positioned behind the user's ear when the device is worn.

25. The device of claim 16, further comprising attachment means selected from the group consisting of adhesive stickers, rubber suction cups, magnetic attachment, silicone straps, clip-on mechanisms, and combinations thereof.

26. A system for monitoring emotional states using brainwave analysis, comprising: the wearable EEG device of any of claims 16-25; and a mobile application configured to receive the transmitted brainwave signals from the wireless communication module, analyze the brainwave signals using machine learning algorithms to determine emotional states of a user, and provide real-time feedback regarding the emotional states to the user through a user interface.

27. The system of claim 26, wherein the machine learning algorithms comprise at least one classifier selected from the group consisting of Support Vector Machines, Random Forest, Neural Networks, K-Nearest Neighbors, and combinations thereof.

28. The system of claim 27, wherein the machine learning algorithms are configured to map the brainwave signals to emotional states using Russell's circumplex model of emotions along axes of arousal and valence.

29. The system of claim 26, wherein the mobile application comprises a self-report interface configured to allow the user to input subjective emotional state data and a live- feed interface configured to display real-time emotional state data derived from the brainwave signals.

30. The system of claim 29, wherein the self-report interface comprises an interactive slider with a color gradient scale ranging from calm to alert states and dynamic text feedback that updates in real-time based on the user's slider position.

31. A method for continuous monitoring of brain activity and emotional state management, comprising: positioning a wearable EEG device comprising an ear cuff around a user's ear; detecting brainwave signals from the user using at least one EEG electrode positioned on the ear cuff; wirelessly transmitting the detected brainwave signals to a mobile application; analyzing the brainwave signals using artificial intelligence algorithms to map the brainwave signals into emotional states; and providing real-time personalized feedback to the user regarding their determined emotional state through a digital neuro-coach.

32. The method of claim 31, wherein the step of analyzing the brainwave signals comprises applying machine learning classifiers selected from the group consisting of Support Vector Machines, Random Forest, Neural Networks, and K-Nearest Neighbors to identify patterns in the brainwave signals corresponding to specific emotional states.

33. The method of claim 32, wherein the machine learning classifiers are configured to map the brainwave signals to emotional states using Russell’s circumplex model of emotions along axes of arousal and valence.

34. The method of claim 31, further comprising a step of collecting self-reported emotional state data from the user through an interactive interface and correlating the selfreported data with the analyzed brainwave signals to improve accuracy of the emotional state determination.

35. The method of claim 34, wherein the step of providing real-time personalized feedback comprises generating customized recommendations for stress management activities based on identified patterns between the self-reported emotional state data and the analyzed brainwave signals.

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