35 results about "Eeg electrodes" patented technology

Methods and systems described herein may comprise a rigid bioelectric patch comprising one or more electrodes disposed on the outside of the rigid bioelectric patch and configured to allow conduction of an electroencephalogram (EEG) signal; and a signal processing unit configured to receive the EEG signal and determine user's medical information. Further comprising a hydrogel configured to be fixed to a user's forehead and conduct the EEG signal to the one or more electrodes, wherein the hydrogel is an adhesive of.01 N / cm − 1,000 N / cm for peel strength and a conductive material of.0001-9:1020 S / m.

Method (100) for calibrating a hearing aid (400) worn by a user, the method (100) being implemented by a processing and control unit (330) of a system (300) for calibrating the hearing aid (400), the method (100) comprising the steps: a) receiving (110) at least one acoustic stimulus; and for each one of the detected acoustic stimulus: b) determining (120) a first set of parameters of such a detected acoustic stimulus, c) receiving (130) at least one EEG response, d) determining (140) a second set of parameters of a respective EEG response, e) performing a first verifying step (150) wherein it is verified whether the first set of parameters and the second set of parameters satisfy or not at least one of predetermined first conditions, f) if the detected at least one EEG response is detected within a certain timeframe from a sound onset time and if the outcome of the first verifying step (150) is positive, storing (151) the detected acoustic stimulus in association with the detected at least one EEG response.

The application particularly relates to a multi-channel portable electroencephalogram (EEG) acquisition device based on FPGA, which comprises a plurality of EEG electrodes, an analog acquisition front end, a digital processing back end based on FPGA and ARM, and a host computer. The analog acquisition front end adopts a multi-channel integrated EEG front end chip to perform amplification, conditioning and analog-to-digital conversion on a plurality of analog EEG signals in parallel, and transmit the digital signals to the digital processing back end through an SPI bus. The digital processing back end comprises an FPGA processing unit and an ARM processing unit, the FPGA internally realizes adaptive notch filtering, FIR low-pass filtering and baseline correction in a parallel pipeline structure, and performs real-time parallel processing on the multi-channel EEG signals; the ARM unit configures parameters through an AXI-Lite bus, and utilizes a DMA mechanism to high-speed carry data to the host computer. The analog domain and the digital domain are partitioned and laid out, independently powered and single-point common-grounded, thereby effectively suppressing digital noise coupling.

This application provides a cross-subject EEG emotion recognition method based on similarity-based dynamic cue routing, belonging to the field of EEG emotion recognition technology. The method includes: acquiring target domain samples and multiple source domain samples formed from subject EEG data and mapping them to target shared latent representation and multiple source domain shared latent representations, respectively; injecting corresponding source domain cue vectors into the source domain shared latent representation of each source domain to obtain enhanced source domain feature representations of multiple source domains; calculating the similarity between each source domain cue vector and the target shared latent representation, converting each vector similarity into sample-level source domain routing weights, and weighting and combining each source domain cue vector to generate dynamic cue; injecting the dynamic cue into the target shared latent representation to obtain enhanced target feature representations; and inputting the enhanced source domain feature representations and enhanced target feature representations of multiple source domains into a multi-branch neural network to train and construct an emotion recognition model.

A system and method for visualizing a position of a needle inside a patient's body for use in providing regional anesthesia and a system and method for providing electro anesthesia during surgery, post surgery and during rehabilitation. The method and system use EMG / AMG information regarding activity of a muscle associated with a target nerve to provide a visualization of a position of a needle or other device relative to the target nerve and to determine efficacy of the electro anesthesia. EMG / AMG information and feedback information such as EEG information may be used to provide automatic adjustment of a waveform provided for electro anesthesia to maintain a suitable pain level.

One embodiment provides a method, including: obtaining EEG data from one or more single channel EEG sensor worn by a user; classifying, using a processor, the EEG data as one of nominal and abnormal; and providing an indication associated with a classification of the EEG data. Other embodiments are described and claimed.

The claimed technical solution relates generally to the field of computing, and more particularly to a device for assessing rhythmic brain activity parameters in real time on a stationary multi-channel electroencephalograph. The technical result consists in faster and more accurate real-time processing of an electroencephalogram (EEG) signal to assess rhythmic brain activity parameters. The claimed technical result is achieved by means of a device for assessing rhythmic brain activity parameters in real time on a stationary multi-channel electroencephalograph, said device comprising: a unit for initially setting and calibrating a brain parameter assessment algorithm, which is designed to be capable of calculating parameters of a rhythmic signal and noise signal model and adjusting the weighting coefficients of a spatial filter on the basis of a fragment of an EEG recording; and a digital multi-channel electroencephalograph containing: a unit for preamplifying and digitizing a calibrated EEG signal, which is designed to be capable of recording low-amplitude brain potentials and also preamplifying and digitizing same; and a signal microprocessor designed to be capable of assessing rhythmic brain activity parameters in real time.

This invention discloses an EEG emotion recognition method based on multi-channel residual convolutional Transformer capsules, belonging to the field of artificial intelligence technology. The method acquires the user's EEG signal, including baseline and stimulus-evoked signals. After baseline correction of the stimulus-evoked signals, multi-scale spatiotemporal feature maps are extracted using a multi-channel residual convolutional network. These maps are then globally correlated and encoded using a Transformer network to obtain a global contextual feature sequence. Finally, these sequences are converted into primary capsule vectors and aggregated into emotion capsule vectors using a dynamic routing protocol. The emotion recognition result is output based on the magnitude of the emotion capsule vectors. By achieving multi-scale spatiotemporal feature fusion through multi-channel residual convolution, capturing global contextual dependencies through Transformer, and improving classification robustness through the dynamic routing mechanism of the capsule network, the method effectively improves the accuracy and generalization ability of EEG emotion recognition.

This invention discloses a dual-domain self-supervised EEG signal representation learning method based on decoupled fusion, belonging to the fields of artificial intelligence and biosignal processing technology. The method includes: acquiring and preprocessing EEG samples, segmenting them into patches; simultaneously extracting and fusing temporal and frequency domain features for each patch; decoupling the features into a temporal-centered view and a spatial-centered view through learnable gating; adding positional encoding and then performing a curriculum-based structured masking strategy; inputting the masked feature stream into a DeFuse encoder, extracting deep spatiotemporal representations through stacked decoupled fusion layers, and predicting the mask content; training the network with a multi-objective loss function including temporal reconstruction loss, spectral fidelity loss, and spatial covariance loss as the optimization objective. This invention can learn structurally complete and generalizable universal representations from unlabeled EEG data, and its performance on downstream tasks such as emotion recognition, motor imagery classification, and anomaly detection is significantly superior to existing methods.

Methods and apparatus are described for synchronizing a stimulus with EEG data for research and clinical and consumer applications using EEG and ECG devices. An input / output adapter for stimulus timing includes an adapter input port for receiving an encoded audio file played from an audio output device. The audio file has a first channel carrying trigger data and a second channel carrying stimulus data. The adapter is configured to separate the encoded audio file into its first and second channels; read the trigger data in the first channel and generate a trigger signal for delivery to an EEG data logger; and read the stimulus data in the second channel and generate an auditory signal for delivery to an audio playback device. Timing errors in regard to stimulus onsets are addressed by the synchronized transmission of the trigger signal and auditory signal.

Systems and methods are disclosed for direct, non-invasive brain-to-AI communication. Consistent with the disclosed technology, a user may silently imagine one sentence chosen from a predefined vocabulary while neural activity is captured with modalities such as high-density fNIRS, MEG. EEG, fMRI or HDDOT. According to other aspects, a machine-learning decoder may classify the neural data and output the corresponding sentence with real- or near-real-time latency. In certain illustrative implementations, the decoded sentence can be forwarded as a natural-language prompt to a large language model, for example, enabling bidirectional conversation, or serve as a discrete command for software, games, or assistive devices. Representative embodiments achieve above-chance accuracy in identifying complete imagined sentences and in detecting imagination onsets. Further, aspects of the disclosed technology provide a scalable, portable and surgery-free pathway to intuitive human-AI interaction, augmentative communication, and hands-free control interfaces.

This application relates to the field of EEG signal processing technology, and provides a method and system for artifact removal from EEG signals, including: acquiring multi-channel EEG signals to be de-artifacted, and constructing a three-dimensional tensor representation of the multi-channel EEG signals; constructing a channel map to characterize the spatial topological relationship of EEG electrodes based on the spatial distribution relationship between the electrodes corresponding to the multi-channel EEG signals; extracting three-dimensional features from the three-dimensional tensor representation using a three-dimensional convolutional neural network; performing cross-channel feature propagation and fusion based on the channel map during the extraction process; obtaining modulation weights for modulating the original complex spectrum based on the three-dimensional features and performing modulation to obtain the temporal representation corresponding to the modulation result; obtaining fused features based on the multi-channel EEG signals, the channel map, and the temporal representation; processing the fused features to obtain the de-artifacted EEG signal. This application can improve the artifact removal effect of EEG signals.

This application discloses a front-end probe and EEG electrodes for use with EEG electrodes, belonging to the field of EEG technology. The front-end probe includes a blocking layer, an absorbent layer, a conductive layer, and an interface layer. The blocking layer forms a receiving cavity, in which the absorbent layer, conductive layer, and interface layer are all disposed. Along the direction from the outer wall to the inner wall of the blocking layer, the absorbent layer, conductive layer, and interface layer are arranged sequentially. The absorbent layer covers at least a portion of the conductive layer, and the conductive layer covers at least a portion of the interface layer. The interface layer is used to connect an elastic support rod of the EEG electrode. Along a first direction, the blocking layer has a top and a bottom opposite each other. The top has a through hole for the elastic support rod to pass through, and the bottom is a closed structure. The side of the blocking layer has an opening opposite to at least a portion of the absorbent layer. The side of the blocking layer is the portion between the top and bottom of the blocking layer.

An EEG-based dementia diagnosis assistance device according to the present embodiment comprises: an EEG data preprocessing unit for preprocessing a collected EEG data set of a test subject, the collected EEG data set being divided into time intervals by setting a window length and a shift length; an Hjorth parameter extraction unit for extracting, for each time interval, Hjorth parameters including an activity index, a mobility index, and a complexity index; and a dementia classification unit for classifying the test subject into one from among a Alzheimer's disease (AD) group, a frontotemporal dementia (FTD) group, and a cognitive normal (CN) group on the basis of the extracted Hjorth parameters. Therefore, AD, FTD, and CN subjects can be classified using Hjorth parameters composed of three major indicators of activity, mobility, and complexity through time domain analysis on EEG datasets.

This invention introduces advanced wearable EEG technology designed for continuous monitoring and analysis of brain activity. It addresses key challenges in signal acquisition, noise reduction, motion artifacts, and long-term user comfort through the use of flexible, soft conductive materials and an innovative electrode architecture featuring an internal cavity that ensures stable skin contact when placed over the mastoid bone behind the ear. The system supports multiple lightweight and adaptable form factors, including ear-cuffs and EEG patches suitable for overnight and extended wear. Optimized shielding, flexible materials, and user-centered mechanical design enable reliable, high-quality brainwave data capture in real-world conditions, overcoming the limitations of traditional rigid EEG systems. This platform enables continuous brainwave monitoring for applications such as cognitive research, neurofeedback, emotional state detection, mental health monitoring, and preventive healthcare. By combining comfort, adaptability, and signal reliability, this invention represents a significant advancement in wearable brain health monitoring technology.

Method (100) for calibrating a hearing aid (400) worn by a user, the method (100) being implemented by a processing and control unit (330) of a system (300) for calibrating the hearing aid (400), the method (100) comprising the steps: a) receiving (110) at least one acoustic stimulus; and for each one of the detected acoustic stimulus: b) determining (120) a first set of parameters of such a detected acoustic stimulus, c) receiving (130) at least one EEG response, d) determining (140) a second set of parameters of a respective EEG response, e) performing a first verifying step (150) wherein it is verified whether the first set of parameters and the second set of parameters satisfy or not at least one of predetermined first conditions, f) if the detected at least one EEG response is detected within a certain timeframe from a sound onset time and if the outcome of the first verifying step (150) is positive, storing (151) the detected acoustic stimulus in association with the detected at least one EEG response.

This invention relates to a integrated reconfigurable smart biophysiological and biophysical sensors platform that can be used in biomedical measurements to increase patient comfort, reduce workload, facilitate data analysis, and obtain more accurate and faster results, comprising; sensors and probes consisting of ECG and EEG electrodes, EMG sensors, carbon dioxide sensor, spirometer sensor, ultrasound probe, spectrophotometer sensor, oximeter sensor, thermistor or thermocouple and pressure sensors (1), an analog-digital converter (2) that converts analog signals from sensors and probes (1) into digital data, a microprocessor (1) that manages all functions of the device, collecting, filtering and analyzing data from sensors and probes (3), a data communication module (4) that enables the transmission of data collected by the device to a computer, mobile device or cloud-based system using Bluetooth, Wi-Fi, ZigBee wireless protocols and a data storage module (5) that allows the data collected by the device to be temporarily or permanently recorded on a memory chip or flash memory (5).

For each of a plurality of time intervals: a power spectrum is generated using a corresponding portion of EEG data; a normalized power spectrum is generated; and a preferred frequency (a frequency with a largest z-score or a highest normalized power in the normalized power spectrum corresponding to the time interval) or dominant frequency for the time interval is determined. A set of time windows is defined, each including multiple time intervals of the plurality of time intervals. For each of a set of time windows, a number of the time intervals is determined for which the preferred or dominant frequency is within a Beta band (12-30 Hz frequencies) or a portion thereof. For a time window, it is determined that an alert condition is satisfied using the number of the plurality of time intervals where the preferred or dominant frequency is within the Beta band or a portion thereof.

An auricular electroencephalogram (EEG) monitoring system may include an EEG recording module having a plurality of EEG sensor electrodes, configured to be coupled to a wearer's ear, and a processing unit configured to analyze EEG data recorded by the EEG recording module to detect presence or cessation of neuropsychiatric disorders of the wearer. An automatic detection-remedy system may include an auricular electroencephalogram (EEG) monitoring system and a transcutaneous auricular vagus nerve stimulation (taVNS) unit having a stimulating electrode in contact with vagus innervated auricular skin of the wearer's ear. When the presence of EEG signals suggestive of the neuropsychiatric disorder is detected by the processing unit, the processing unit is configured to immediately send signals to the taVNS unit to automatically start sending pre-determined electric stimuli to the vagus innervated auricular skin of the wearer's ear.

This utility model discloses an infrared electroencephalogram (EEG) electrode positioning device, comprising: a fixing component, including a fixing surface and an assembly surface, wherein the fixing surface is used for detachable fixing to a wall, and the assembly surface is provided with a first ball joint; an infrared probe for projecting red dots to the target electrode position; and an adjusting rod assembly, one end of which is rotatably connected to the first ball joint of the fixing component, and the other end of which is movably connected to the infrared probe. This utility model achieves detachable fixing to the wall through the fixing component, eliminating the need for drilling and preventing damage to the installation environment, thus solving the problems caused by traditional drilling fixing methods. The rotatable connection of one end of the adjusting rod assembly to the first ball joint of the fixing component allows the adjusting rod assembly to be adjusted in multiple directions. Compared with traditional rigid structure supports that only support single-direction adjustment, this better adapts to the multi-angle needs of different patient head shapes or complex surgical scenarios, improving the adjustability and flexibility of the support.

This invention discloses a domain incremental epilepsy detection system and method based on knowledge data fusion, belonging to the field of brain-computer interfaces and machine learning. The system includes: a preprocessing module for defining a single EEG data set as an independent domain, and a training set composed of multiple sequentially collected EEG data sets; a training module for training a domain incremental epilepsy detection model using the training set, including a knowledge-guided parameter isolation module, a feature extraction network, a data fusion module, and a classifier; and a detection module for inputting the EEG data of the test subject into the trained domain incremental epilepsy detection model to obtain classification results. For sequentially input data, this invention uses the distance between the local domain prototype of the new input domain and each local domain prototype in the global prototype pool as the similarity criterion. Similar prototypes are merged, while dissimilar prototypes are directly added, allowing the model to simultaneously consider plasticity and stability, making it applicable to continuous learning scenarios.

This invention discloses an emotion recognition method based on EEG and eye movement signals using modality generation, comprising the following steps: manual feature extraction; extraction of EEG spatial features; extraction of EEG temporal features; extraction of eye movement spatiotemporal features; feature concatenation and modality alignment; modality fusion; pre-training; and model testing. This invention introduces a modality generation pre-training task. Modality generation aims to generate a label for another modality given one modality as a condition. With the help of the pre-training mechanism, the model can learn robust and universal feature representations that adapt to various input conditions, effectively solving the accuracy decline problem caused by the missing modalities in multimodal emotion recognition, and significantly enhancing the model's adaptability to both modality-complete and modality-missing scenarios.

The present invention relates to the acquisition, processing, and monitoring of signals, and particularly to the acquisition, processing, and monitoring of electrophysiological signals. More particularly, the present invention relates to the acquisition, processing, and monitoring electroencephalography (EEG) signals representing cortical / brain activity. Further, the present invention relates to a method and apparatus for acquiring such signals in the presence of electrical interference and noise. More particularly, the present invention relates to systems and methods for filtering out and rejecting electrical interference and noise while maintaining or improving the quality of the underlying physiological signal and preventing perturbation or introduction of artifacts into the physiological signal.

The disclosure provides systems and methods for automatically titrating an electrical pulse amplitude for a patient-implanted VNS stimulator. One or more external sensors (e.g., EEG, EKG, EMG, auditory sensors, inertial motion sensors, etc.) can be applied to the patient to generate data relevant to an acceptable amplitude of the electrical pulse for a given cathode in a multi-cathode cuff. In one embodiment, the device may include a controller on the implanted VNS stimulator that receives data, e.g., using a wireless connection, from the external sensors and titrates upward the amplitude until an acceptable amplitude is determine that provides efficacy with minimal, if any, side effects.

A method (400) for delivering user-specific audio output to a user is disclosed. The method (400) includes receiving audio input from a user device (106) associated with the user. Further, the method (400) includes identifying auditory stimulus from the audio input. The auditory stimulus being indicative of one or more biomarkers that trigger a neural response from the brain of the user. Furthermore, the method (400) includes obtaining, from the one or more biosignal electrodes (104), electroencephalogram (EEG)-based neural response data corresponding to the auditory stimulus. Moreover, the method (400) includes identifying a frequency range associated with altered response patterns based on the (EEG)-based neural response data. The method (400) further includes modulating amplitude corresponding to the frequency range. Finally, the method (400) includes delivering, via a speaker unit (214) of a wearable audio output apparatus (102), a user-specific audio output to the user based on the modulation.

Systems, methods, and computer-readable storage media for transcranial electrical stimulation, and more particularly to systems, methods and software for personalized transcranial electrical stimulation using electroencephalographic features. A system can include an electroencephalography (EEG) system, a feature extraction module, an electrical stimulation target module, and an electrical stimulation system. The EEG system can include sensor electrodes to be attached to a subject's scalp and which provide electrical signals. The electrical signals are processed by the feature extraction module, resulting in corresponding electrical signals for a first period of time and features therefrom. The electrical stimulation target designation module can use those to determine stimulation parameters which result in electrical stimulations provided via the electrodes to the subject's scalp.

This invention relates to the field of sleep health assessment technology, specifically disclosing a sleep health assessment and management method and system based on AI and sensor data. The method includes: continuously transmitting frequency-modulated continuous waves via millimeter-wave radar and receiving human body reflection signals; analyzing the human body reflection signals based on an AI model to obtain first sleep data; acquiring brainwave data from the user via EEG electrodes; obtaining second sleep data based on the brainwave data; acquiring physiological parameter data from the user via a sensor array; obtaining third sleep data based on the physiological parameter data; establishing a user sleep model based on the differences between the first, second, and third sleep data; and assessing the user's sleep health status based on the user sleep model. This invention employs three methods of monitoring the user's sleep state in synergy, which can eliminate the influence of erroneous data and improve the accuracy of the user sleep model.

For each of a plurality of time intervals: a power spectrum is generated using a corresponding portion of EEG data; a normalized power spectrum is generated; and a preferred frequency (a frequency with a largest z-score or a highest normalized power in the normalized power spectrum corresponding to the time interval) or dominant frequency for the time interval is determined. A set of time windows is defined, each including multiple time intervals of the plurality of time intervals. For each of a set of time windows, a number of the time intervals is determined for which the preferred or dominant frequency is within a Beta band (12-30 Hz frequencies) or a portion thereof. For a time window, it is determined that an alert condition is satisfied using the number of the plurality of time intervals where the preferred or dominant frequency is within the Beta band or a portion thereof.

The invention relates to a system and method for optimizing task assignments in an enterprise environment using real-time biometric data collected from wearable devices. The system comprises a wearable device for capturing biometric signals such as heart rate, EEG data, and other physiological metrics, a data processor to filter and validate the data, a cognitive analyzer to calculate cognitive metrics including cognitive load, stress levels, and focus, and a workflow optimizer to assign tasks based on these metrics. The workflow optimizer leverages machine learning algorithms to analyze the cognitive state of users, compare it with historical performance data, and adjust workload distribution dynamically. The system integrates with enterprise resource planning (ERP) systems to synchronize optimized task assignments across the organization. By monitoring cognitive metrics in real time, the invention ensures efficient workload management, reduces employee fatigue, and enhances overall productivity.

Disclosed herein is a system and method implementing an automated, generalizable model for tracking cortical spreading depressions using EEG. The model comprises convolutional neural networks and graph neural networks to leverage both the spatial and the temporal properties of CSDs in the detection. The trained model is generalizable to different head models such that it can be applied to new patients without re-training. Further, the model is scalable to different densities of EEG electrodes, even when trained on a specific electrode density.