Wireless wearable high-speed emotion detection brain-computer interaction system

Abstract

The invention discloses a wireless wearable high-speed emotion detection brain-computer interaction system, which comprises a scalp electroencephalogram acquisition module, a real-time scalp impedance intelligent monitoring self-adaptive module, a signal transmission module, a cloud platform calculation module, a terminal display module and an internet-of-things equipment interaction linkage module. By reducing scalp impedance and optimizing electrode design and a signal processing algorithm, the electroencephalogram signal acquisition quality is effectively improved, noise and interference are reduced, signals are clearer and more accurate, a dry electrode and wireless transmission technology is adopted, an electroencephalogram signal acquisition system is more portable, a user can acquire electroencephalogram signals in different scenes, and the user experience is improved. Daily use and mobile monitoring are facilitated, the cloud platform has strong parallel processing capacity, a large amount of electroencephalogram signal data can be processed and analyzed in real time, and the accuracy and efficiency of data analysis are improved in combination with the machine learning technology.

Description

Technical Field

[0001] The present invention relates to a system, device and process using brain-computer interface technology, and is used for a commercial device system for real-time emotion detection and brain-computer interaction. Background Art

[0002] Mood disorders affect hundreds of millions of people worldwide and are one of the major public health issues. At present, the detection of mood disorders mainly relies on clinical evaluation by doctors and the use of psychological scale measurement tools, but these evaluation methods are highly subjective and lack objective evaluation indicators. They cannot objectively judge the effects of medication and other therapies, and cannot conduct large-scale screening and early prevention.

[0003] Among the physiological and non-physiological signals that can be collected by wearable devices, EEG is the most emotionally relevant, most representative, and hard to disguise signal. Using wearable devices to collect EEG data is currently a new method for monitoring emotional disorders.

[0004] This invention provides a wireless wearable high-rate emotion detection brain-computer interaction system, which solves the problem that existing emotion disorder detection methods are highly subjective and lack objective evaluation indicators, realizes real-time and objective detection of emotional states, and provides personalized emotion management solutions. Summary of the invention

[0005] The purpose of the present invention is to provide a wireless wearable high-rate emotion detection brain-computer interaction system to solve the problem proposed in the above background technology that the existing emotional disorder detection methods are highly subjective and lack objective evaluation indicators, realize real-time and objective detection of emotional states, and provide personalized emotion management solutions.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a wireless wearable high-rate emotion detection brain-computer interaction system, comprising a scalp EEG acquisition module, a real-time scalp impedance intelligent monitoring and adaptation module, a signal transmission module, a cloud platform computing module, a terminal display module and an IoT device interactive linkage module.

[0007] Preferably, the scalp EEG acquisition module adopts a dry electrode EEG sensor, specifically comprising an electrode material of silver, silver chloride Ag, or AgCl gold-plated material, an electrode structure of a combination of needle electrodes and flexible electrodes to adapt to the scalp curve, a ring-type electrode head is combined into a position electrode array, a built-in high-sensitivity amplification circuit including a preamplifier, a filtering circuit, and an analog-to-digital converter, an integrated wireless transmission module using Bluetooth or Wi-Fi technology, equipped with an adjustable head-mounted device, a lightweight lithium battery power system, and software with signal filtering, preprocessing, visualization, and interpretation functions, and a reference electrode is placed on the earlobe.

[0008] Preferably, the real-time scalp impedance intelligent monitoring adaptive module has impedance monitoring and calibration functions, which enables the user to adjust the electrode position or headband pressure by displaying the electrode impedance value, and adopts flexible electrodes, comb electrodes, electrode pressure adjustment, skin pretreatment and the use of high input impedance amplifiers to reduce scalp impedance, and uses adaptive electrodes to adjust the contact area according to the scalp shape and measure the scalp impedance through the three-point method.

[0009] Preferably, the signal transmission module includes low-pass filtering, high-pass filtering, and band-pass filtering for the signal, uses a high-bit analog-to-digital converter to convert the signal, performs artifact removal processing through a digital signal processor, transmits the signal wirelessly and uses low-power, low-latency technology and data redundant transmission and compression algorithms to solve high impedance, noise, signal delay and packet loss, and energy consumption management problems, and uses intelligent signal processing algorithms and adaptive wireless transmission.

[0010] Preferably, the cloud platform computing module includes collecting EEG signals and transmitting them to the cloud wirelessly, compressing and uploading data, encrypting and storing data, processing and analyzing signals, and having powerful parallel processing capabilities for real-time data processing and decision support. The results are presented through a visual interface, and users can access it through a mobile phone APP or browser to perform long-term data analysis and machine learning as well as training and optimization of AI models. It adopts cloud computing infrastructure, edge computing and cloud collaboration, artificial intelligence and deep learning, federated learning, big data storage and analysis, and security and privacy protection key technologies.

[0011] Preferably, the terminal display module displays the cloud platform calculation results in a graphical, statistical and interactive manner, including EEG waveform diagrams, spectrum analysis diagrams, EEG topology diagrams, brain wave classification results, EEG signal statistical charts, provides user interface and interaction and personalized reports, and has adaptive display and intelligent reminder functions.

[0012] Preferably, the IoT device interactive linkage module extracts characteristic bands based on EEG signal frequency analysis, identifies the user's emotional state through an algorithm and converts it into instructions, connects the IoT devices via wireless communication technology and transmits instructions through the IoT protocol, thereby realizing interaction with the Adelsomnia machine, emotional diffuser, and emotion-driven doll.

[0013] Compared with the prior art, the present invention provides a wireless wearable high-rate emotion detection brain-computer interaction system, which has the following beneficial effects:

[0014] In this wireless wearable high-rate emotion detection brain-computer interaction system, the following benefits can be achieved:

[0015] Improve signal quality: By reducing scalp impedance, optimizing electrode design and signal processing algorithms, the acquisition quality of EEG signals is effectively improved, noise and interference are reduced, and the signal is clearer and more accurate.

[0016] Enhanced portability: The use of dry electrodes and wireless transmission technology makes the EEG signal acquisition system more portable. Users can collect EEG signals in different scenarios, which facilitates daily use and mobile monitoring.

[0017] Efficient data analysis and processing: The cloud platform has powerful parallel processing capabilities and can process and analyze large amounts of EEG signal data in real time. Combined with machine learning technology, it improves the accuracy and efficiency of data analysis.

[0018] Improve user experience: The terminal display module provides a friendly user interface and interactive settings, and displays analysis results in a variety of ways, allowing users to intuitively understand their brain activity status. It also supports a variety of terminal devices to meet the needs of different users.

[0019] Ensure data security: Use security measures such as encrypted storage, user identity authentication and authorization to protect users' EEG data and personal privacy, and ensure data security and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a system framework diagram of the present invention.

[0021] Figure 2 The figure is a flowchart of the system of the present invention.

[0022] Figure 3 This is a main body diagram of the EEG acquisition device of the present invention.

[0023] Figure 4 A main interface is set for the wearable high-rate emotion detection brain-computer interaction system of the present invention.

[0024] Figure 5 It is the main interface for data conversion of the wearable high-rate emotion detection brain-computer interaction system of the present invention.

[0025] Figure 6 This is the dry electrode impedance query and adjustment interface of the wearable high-rate emotion detection brain-computer interaction system of the present invention.

[0026] Figure 7 This is the 8-lead EEG detection waveform of the wearable brain-computer emotion detection system of the present invention Figure 1 .

[0027] Figure 8 This is the 8-lead EEG detection waveform of the wearable brain-computer emotion detection system of the present invention Figure 2 .

[0028] Fig. 9 This is a spectrum diagram of the brain-to-brain interaction emotion detection system of the present invention.

[0029] Fig.10This is the PSD spectrum diagram of the brain-to-brain interaction emotion detection system of the present invention.

[0030] Fig.11 The wearable brain-interaction emotion detection system of the present invention has an EEG topography. Figure 1 .

[0031] Fig.12 The wearable brain-interaction emotion detection system of the present invention has an EEG topography. Figure 2 .

[0032] Fig.13 For the present invention Figure 3 Schematic diagram of the structure with the Bluetooth receiver and adjustable head-mounted device removed.

[0033] In the figure: 1. Scalp EEG acquisition module; 2. Real-time scalp impedance intelligent monitoring and adaptive module; 3. Signal transmission module; 4. Cloud platform computing module; 5. Terminal display module; 6. IoT device interactive linkage module; 7. EEG signal acquisition electrode; 8. EEG signal grounding electrode; 9. EEG signal reference electrode; 10. Bluetooth receiver; 11. Adjustable wearable device. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] The present invention provides Figure 1-13A wireless wearable high-rate emotion detection brain-computer interaction system is shown, comprising a scalp EEG acquisition module 1, a real-time scalp impedance intelligent monitoring and adaptation module 2, a signal transmission module 3, a cloud platform computing module 4, a terminal display module 5 and an IoT device interactive linkage module 6. The scalp EEG acquisition module 1 adopts a dry electrode EEG sensor, which specifically includes an electrode material of silver, silver chloride Ag, or AgCl gold-plated material. The electrode structure adopts a combination of needle electrodes and flexible electrodes to adapt to the scalp curve. The ring-type electrode head is combined into a part electrode array. The built-in high-sensitivity amplification circuit includes a preamplifier, a filtering circuit, and an analog-to-digital converter. The integrated wireless transmission module adopts Bluetooth or Wi-Fi technology and is equipped with a Adjust the head-mounted device, lightweight lithium battery power system and software with signal filtering, preprocessing, visualization and interpretation functions. The reference electrode is placed on the earlobe. The real-time scalp impedance intelligent monitoring adaptive module 2 has impedance monitoring and calibration functions. By displaying the electrode impedance value, the user can adjust the electrode position or headband pressure. Flexible electrodes, comb electrodes, electrode pressure adjustment, skin pretreatment and high input impedance amplifier measures are adopted to reduce scalp impedance. Adaptive electrodes are used to adjust the contact area according to the scalp shape and the scalp impedance is measured by the three-point method. The signal transmission module 3 includes low-pass filtering, high-pass filtering, and band-pass filtering of the signal, and a high-bit analog-to-digital converter is used for signal conversion. Through digital signal processing The device removes artifacts, transmits signals wirelessly and adopts low-power, low-latency technology and data redundant transmission and compression algorithms to solve high impedance, noise, signal delay and packet loss, and energy management problems. It adopts intelligent signal processing algorithms and adaptive wireless transmission. The cloud platform computing module 4 includes collecting EEG signals and transmitting them to the cloud wirelessly, compressing and uploading data, encrypting and storing data, processing and analyzing signals, and has powerful parallel processing capabilities for real-time data processing and decision support. The results are presented through a visual interface, and users can access them through mobile phone APP or browser for long-term data analysis and machine learning as well as training and optimization of AI models. It adopts cloud computing infrastructure, edge computing and cloud Collaboration, artificial intelligence and deep learning, federated learning, big data storage and analysis, security and privacy protection key technologies, the terminal display module 5 displays the cloud platform calculation results in a graphical, statistical and interactive way, including EEG waveform diagram, spectrum analysis diagram, EEG topology diagram, brain wave classification results, EEG signal statistical charts, provides user interface and interaction and personalized reports, and has adaptive display and intelligent reminder functions. The IoT device interactive linkage module 6 extracts characteristic bands based on EEG signal frequency analysis, and converts the user's emotional state into instructions through algorithm recognition. It connects IoT devices through wireless communication technology and transmits instructions through IoT protocol to realize interaction with Adeshu sleep machine, emotional diffuser, and emotion-driven doll.

[0036] Preferably, an adjustable wearable device 11 is provided on the middle upper side of the scalp EEG acquisition module 1, and a plurality of EEG signal acquisition electrodes 7, an EEG signal grounding electrode 8 and a plurality of EEG signal reference electrodes 9 are provided on the inner side of the scalp EEG acquisition module 1, wherein the EEG signal reference electrode 9 is used to contact the user's earlobe, and the EEG signal grounding electrode 8 is provided on the inner side of both ends of the scalp EEG acquisition module 1, and the plurality of EEG signal acquisition electrodes 7 are respectively provided on the front and rear sides of the scalp EEG acquisition module 1 and the contact parts of the back of the user's head and forehead, as well as the front and rear ends of the adjustable head-mounted device 11.

[0037] In addition, the scalp EEG acquisition module 1 is provided with a Bluetooth receiver 10 for information transmission via Bluetooth.

[0038] Preferred 1. Scalp EEG acquisition module 1

[0039] Electrode material selection and production:

[0040] Silver / silver chloride (Ag / AgCl) gold-plated material is selected as the electrode material that directly contacts the scalp. Through a specific processing technology, the stability and conductivity of the electrode are ensured.

[0041] For the needle electrodes and flexible electrodes, appropriate manufacturing technologies are used respectively to ensure that they can adapt well to the scalp curve and provide stable signal acquisition.

[0042] Electrode structure design and assembly:

[0043] The needle-shaped electrodes are combined with flexible electrodes that better fit the scalp curve and designed into a specific structure. For example, the needle-shaped electrodes can be distributed around the flexible electrodes to increase the stability of the contact.

[0044] The circle-type electrode head is combined into a local electrode, and the electrodes of multiple locations form an array. According to the activity characteristics of different brain areas, the electrode array is reasonably arranged to ensure that the EEG signals can be fully collected.

[0045] Use flexible conductive sponge to cover the irregular curved surface of the scalp to increase the contact area and fit between the electrode and the scalp.

[0046] Signal amplification and processing circuit integration:

[0047] A high-sensitivity amplifier circuit is built into the dry electrode sensor. The preamplifier is responsible for amplifying the weak signal received from the electrode and reducing noise interference. The filter circuit is used to filter out environmental noise and irrelevant signals of the electromyographic signal to improve the clarity of the EEG data. The analog-to-digital converter (ADC) converts the amplified analog signal into a digital signal for subsequent processing and storage.

[0048] The amplifier circuit is optimized to ensure stable operation in a high impedance environment while minimizing power consumption.

[0049] Data transmission module settings:

[0050] The dry electrode EEG sensor integrates wireless transmission modules, such as Bluetooth and Wi-Fi wireless technologies. Set appropriate transmission parameters to ensure the accuracy and stability of the signal.

[0051] Conduct compatibility testing of data transmission to ensure seamless connection with different external devices (such as computers and mobile phones).

[0052] Headset design and adjustment:

[0053] The adjustable bracket is designed to accommodate different head sizes and provide a better fit. The comfortable material is used to reduce the wearer's discomfort.

[0054] Perform stability tests on the headset to ensure that the electrodes maintain good contact with the scalp during various activities.

[0055] Power system configuration:

[0056] The dry electrode EEG sensor integrates a lightweight lithium battery power system. High energy density lithium batteries are selected to ensure sufficient power for the amplifier, processing circuit and data transmission module.

[0057] Design efficient power management circuits to optimize battery efficiency and extend battery life. Use low-energy communication protocols to reduce power consumption.

[0058] Software and data processing development:

[0059] Develop dedicated software for EEG signal analysis. The software has signal filtering and preprocessing functions to remove artifacts and noise and improve data quality. The collected EEG signals are displayed as waveforms in real time to help users understand brain activity.

[0060] The signal interpretation function is implemented in specific application software, which can interpret brain wave patterns and convert them into user commands.

[0061] Reference electrode placement:

[0062] The reference electrode is usually placed on the earlobe to serve as a stable reference for the electrical signal. Make sure the reference electrode connection is stable to reduce interference.

[0063] 2. Real-time scalp impedance intelligent monitoring adaptive module 2

[0064] Impedance Monitoring and Calibration Implementation:

[0065] In the dry electrode EEG system, a real-time impedance monitoring function is set up. The impedance value of each electrode is monitored in real time through the sensor and displayed on the device's display or through a connected device (such as a mobile phone APP).

[0066] Users can instantly adjust the electrode position or headband pressure according to the displayed impedance value to ensure that the impedance is within a reasonable range. In general, the impedance of the dry electrode should be kept below 50kΩ to ensure signal quality. If the impedance is too high, the system will issue a prompt asking the user to adjust the electrode or reset it.

[0067] Measures to reduce scalp impedance:

[0068] Skin pretreatment: Use the method of gently wiping the scalp to remove part of the cuticle and oil, which can significantly reduce the impedance. Choose the appropriate wiping material to ensure that it is non-irritating to the scalp. At the same time, formulate a standardized wiping operation process to ensure the consistency of the pretreatment effect.

[0069] Use high input impedance amplifier: Select a high input impedance amplifier to reduce the impact of high impedance on signal acquisition and maintain stable signal transmission under high impedance conditions. Optimize the parameters and perform performance tests on the amplifier to ensure that it can work properly under different impedance conditions.

[0070] Adaptive electrode design and application:

[0071] The contact area is adaptively adjusted according to the shape of the scalp, and the electrode shape and spring structure are designed. The sensor monitors the changes in the scalp shape in real time, and the electrode can automatically adjust to maintain good contact.

[0072] Conduct performance tests on adaptive electrodes, including signal stability and impedance changes under different scalp shapes and motion conditions. Continuously optimize electrode design and control algorithms to improve adaptability and reliability.

[0073] Implementation of the three-point method for measuring scalp impedance:

[0074] Use three electrodes to measure the contact impedance between the electrode and the scalp. Arrange the positions of the three electrodes reasonably to ensure the accuracy and stability of the measurement.

[0075] First, an AC current of known frequency and amplitude (such as 1kHz, current in the microampere to milliampere level) is applied through the current source electrode. Then, the voltage difference caused by the current passing through the scalp is measured between the measuring electrode and the reference electrode. The reference electrode provides a stable potential reference to reduce the influence of external noise.

[0076] Finally, the impedance value is calculated using the formula: R = V / I, where R is the scalp impedance, V is the measured voltage, and I is the applied current. Develop the corresponding calculation software to calculate and display the scalp impedance value in real time.

[0077] 3. Signal transmission module 3

[0078] Signal filtering operations:

[0079] While amplifying the signal, the dry electrode EEG system adds a filter to remove noise. Set the low-pass filter parameters to remove high-frequency noise (such as electromyographic noise and electromagnetic interference), usually set below 30-100Hz.

[0080] Set the high-pass filter parameters to remove low-frequency interference (such as skin impedance changes or slow device drift), usually set at 0.1Hz or lower.

[0081] Bandpass filtering is performed to limit the signal to the commonly used EEG frequency band (usually between 0.1Hz and 30Hz), thereby filtering out external electromagnetic interference (such as 50 / 60Hz power supply noise). The performance of the filter is tested to ensure that it can effectively remove various noises.

[0082] Analog-to-digital conversion is performed:

[0083] The amplified and filtered analog signal is converted into a digital signal through an analog-to-digital converter (ADC). Choose a suitable analog-to-digital converter and ensure that its sampling rate is between 250Hz and 1000Hz, which ensures that enough signal details are captured.

[0084] Use high-bit conversion (such as 16-bit, 24-bit ADC) to provide more precise signal resolution, ensuring that even in high-impedance environments, small changes in the signal can be maintained. Calibrate and test the analog-to-digital converter to ensure its conversion accuracy and stability.

[0085] Digital Signal Processing Operations:

[0086] The converted digital signal enters the digital signal processor for further signal processing. Independent component analysis (ICA), adaptive filtering and other algorithms are used to remove motion artifacts and ensure signal purity.

[0087] Perform performance tests on artifact removal, compare signal quality before and after processing, and continuously optimize algorithm parameters.

[0088] Signal transmission implementation:

[0089] Dry electrode EEG signals are usually transmitted wirelessly to the data acquisition device (computer). Choose a suitable wireless transmission module (such as Bluetooth, Wi-Fi) to achieve portable and unfettered data transmission.

[0090] Long-duration lithium battery is used to ensure that the battery can meet the needs of long-term signal transmission. At the same time, the wireless transmission protocol is optimized to avoid transmission delays or data packet loss. The stability and reliability of wireless transmission are tested to ensure that it can work normally in different environments.

[0091] Data storage and analysis operations:

[0092] After being transmitted to the data acquisition device, the EEG signal will be stored and analyzed in real time. Select appropriate storage devices and database systems to record data in the form of time series for subsequent research such as brain wave processing and pattern recognition.

[0093] Conduct real-time analysis, extract specific brain wave features, and perform interactive operations or medical diagnosis. Develop data analysis software to achieve automated data analysis and result display.

[0094] 4. EEG signal acquisition cloud platform computing module 4

[0095] EEG signal acquisition and transmission settings:

[0096] The EEG signals collected by the electrode array are transmitted to the cloud wirelessly after local signal preprocessing (amplification, filtering, analog-to-digital conversion, etc.). Low-latency and high-reliability wireless transmission technology is selected to ensure that the signals can be transmitted to the cloud in real time and accurately. Data encryption and compression mechanisms are set up to ensure data security and transmission efficiency.

[0097] The transmission process is monitored in real time to ensure the stability and integrity of the signal. Once a transmission interruption or data loss occurs, the system can automatically retransmit or alarm.

[0098] Data compression and upload operations:

[0099] Since the amount of EEG signal data is large, in order to improve transmission efficiency and save cloud storage space, data compression is performed before uploading to the cloud. Lossless or lossy compression algorithms are used to balance data quality and compression ratio according to actual needs.

[0100] Develop efficient data compression software to minimize data volume without affecting signal quality. At the same time, optimize the upload process to ensure that data can be uploaded to the cloud server quickly and stably.

[0101] Encrypted storage implementation:

[0102] In order to protect the privacy and data security of users, EEG signal data is encrypted and stored in the cloud. Advanced encryption algorithms, such as symmetric encryption algorithms such as AES or asymmetric encryption algorithms such as RSA, are used to ensure that data is not illegally accessed or tampered with during storage.

[0103] Establish a strict access control mechanism so that only authorized users can access and use EEG data. At the same time, regularly back up and restore data to prevent data loss or damage.

[0104] Signal processing and analysis performed:

[0105] The cloud server has powerful parallel processing capabilities and can process and analyze a large amount of EEG signal data in real time. It uses advanced signal processing algorithms and machine learning technology to extract characteristic information from EEG signals, such as frequency, amplitude, phase, etc.

[0106] Classify and identify EEG signals to determine the user's emotional state, attention level, etc. Develop visual data analysis tools to help users intuitively understand the analysis results of EEG data.

[0107] Results presentation and user access settings:

[0108] The EEG signal analysis results are presented to the user through a visual interface. EEG waveforms, spectrum analysis graphs, EEG topology graphs, brain wave classification results, EEG signal statistics graphs, etc. can be displayed graphically, statistically, and interactively.

[0109] Users can access the cloud platform through a mobile app or browser to view their EEG data and analysis results. It provides personalized reports and suggestions to help users better understand their brain status and health status.

[0110] Long-term data analysis and machine learning operations:

[0111] The cloud platform can store and analyze the user's EEG data for a long time and accumulate a large number of data samples. These data can be used to train and optimize machine learning and artificial intelligence models to improve the accuracy and reliability of EEG signal analysis.

[0112] Use deep learning algorithms, such as convolutional neural networks and recurrent neural networks, to extract and classify EEG signals. Continuously optimize the parameters and structure of the model to meet the needs of different users and data characteristics.

[0113] Key technology applications:

[0114] Adopt cloud computing infrastructure to ensure high availability, scalability and security of the cloud platform. Use distributed storage and computing technology to improve the efficiency and speed of data processing.

[0115] By combining edge computing with cloud collaboration technology, some data processing tasks can be delegated to local devices or edge servers to reduce computing pressure and transmission delays in the cloud.

[0116] Apply artificial intelligence and deep learning technologies to continuously improve the intelligence level of EEG signal analysis. At the same time, use privacy protection technologies such as federated learning to ensure the security and privacy of user data.

[0117] 5. Terminal display module 5

[0118] Result display design:

[0119] The EEG signal analysis results are displayed in a graphical way, such as EEG waveform diagram, spectrum analysis diagram, EEG topology diagram, etc. Using clear and intuitive charts and images, users can quickly understand the status of their brain activity.

[0120] Provides statistical results display, such as the mean, variance, standard deviation and other statistical indicators of EEG signals. Through data analysis and comparison, it helps users understand the difference between their EEG data and the normal range.

[0121] Design an interactive display interface so that users can view different EEG data and analysis results by clicking, sliding, etc. Provide detailed explanations and instructions to help users better understand the meaning of EEG signals.

[0122] User interface and interaction settings:

[0123] Implement an interactive feedback mechanism, where users can provide feedback on the analysis results through buttons or gestures on the interface. For example, users can confirm or deny the accuracy of the analysis results, and the system can make further optimizations and adjustments based on user feedback.

[0124] Design a multi-language interface to meet the needs of different users. At the same time, consider the vision and operating habits of different users and provide adjustable font size, contrast and other settings.

[0125] Terminal equipment selection and adaptation:

[0126] Supports a variety of terminal devices, such as smartphones, tablets, smart watches, etc. Optimize and adapt the interface according to the screen size and performance characteristics of different devices to ensure a good user experience on different devices.

[0127] Conduct device compatibility testing to ensure that the system can seamlessly connect and operate normally with various mainstream terminal devices. At the same time, provide guidance and assistance for device connection to help users get started quickly.

[0128] Real-time update and synchronization:

[0129] Ensure that the terminal display module 5 can update the EEG signal analysis results in real time. Through real-time communication with the cloud server, data synchronization and update are achieved, allowing users to understand their brain activity status at any time.

[0130] Design a data cache mechanism to display the latest analysis results even when the network connection is unstable or interrupted. Once the network is restored, the system can automatically synchronize and update the data.

[0131] Security and Privacy Protection:

[0132] Strict security measures are taken to protect users' EEG data and personal privacy. Communications between terminal devices and cloud servers are encrypted to prevent data from being stolen or tampered with.

[0133] Establish user identity authentication and authorization mechanisms so that only authorized users can access their EEG data. At the same time, clarify the scope and purpose of user data use to ensure users’ right to know and right to choose.

[0134] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A wireless wearable high-rate emotion detection brain-computer interaction system, characterized by: It comprises a scalp electroencephalogram acquisition module (1), a real-time scalp impedance intelligent monitoring and adaptive module (2), a signal transmission module (3), a cloud platform computing module (4), a terminal display module (5) and an Internet of Things device interactive linkage module (6).

2. A wireless wearable high-rate emotion detection brain-computer interaction system according to claim 1, characterized in that: The scalp EEG acquisition module (1) adopts a dry electrode EEG sensor, which specifically includes an electrode material of silver or silver chloride-plated gold material, an electrode structure of a combination of a needle electrode and a flexible electrode to adapt to the scalp curve, a ring-shaped electrode head combined into a position electrode array, a built-in high-sensitivity amplification circuit including a preamplifier, a filter circuit, and an analog-to-digital converter, an integrated wireless transmission module using Bluetooth or Wi-Fi technology, an adjustable head-mounted device, a lightweight lithium battery power system, and software with signal filtering, preprocessing, visualization, and interpretation functions, and a reference electrode placed on the earlobe.

3. A wireless wearable high-rate emotion detection brain-computer interaction system according to claim 1, characterized in that: The real-time scalp impedance intelligent monitoring adaptive module (2) has impedance monitoring and calibration functions, and enables the user to adjust the electrode position or headband pressure by displaying the electrode impedance value, and adopts flexible electrodes, comb electrodes, electrode pressure adjustment, skin pretreatment and high input impedance amplifier measures to reduce scalp impedance, and adopts adaptive electrodes to adjust the contact area according to the scalp shape and measure the scalp impedance through the three-point method.

4. A wireless wearable high-rate emotion detection brain-computer interaction system according to claim 1, characterized in that: The signal transmission module (3) includes low-pass filtering, high-pass filtering, and band-pass filtering for the signal, using a high-bit analog-to-digital converter to convert the signal, performing artifact removal processing through a digital signal processor, transmitting the signal wirelessly and using low-power, low-latency technology and data redundancy transmission and compression algorithms to solve the problems of high impedance, noise, signal delay and packet loss, and energy consumption management, and using intelligent signal processing algorithms and adaptive wireless transmission.

5. The wireless wearable high-rate emotion detection brain-computer interaction system according to claim 1, characterized in that: The cloud platform computing module (4) includes collecting EEG signals and transmitting them to the cloud via wireless, compressing and uploading data, encrypting and storing, processing and analyzing signals, having powerful parallel processing capabilities for real-time data processing and decision support, presenting results through a visual interface, and allowing users to access the module through a mobile phone APP or browser, performing long-term data analysis and machine learning as well as training and optimization of AI models, and using cloud computing infrastructure, edge computing and cloud collaboration, artificial intelligence and deep learning, federated learning, big data storage and analysis, and security and privacy protection key technologies.

6. A wireless wearable high-rate emotion detection brain-computer interaction system according to claim 1, characterized in that: The terminal display module (5) displays the cloud platform calculation results in a graphical, statistical and interactive manner, including EEG waveform diagrams, spectrum analysis diagrams, EEG topology diagrams, EEG classification results, EEG signal statistical diagrams, provides user interface and interaction and personalized reports, and has adaptive display and intelligent reminder functions.

7. A wireless wearable high-rate emotion detection brain-computer interaction system according to claim 1, characterized in that: The IoT device interactive linkage module (6) extracts characteristic bands based on EEG signal frequency analysis, identifies the user's emotional state through an algorithm and converts it into instructions, connects the IoT devices through wireless communication technology and transmits instructions through the IoT protocol, thereby realizing interaction with the Adelsomnia machine, the emotional diffuser, and the emotionally driven doll.

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