384 results about "Eeg electrodes" patented technology

Disclosed is an EEG recognition method for a natural hand movement based on a time-domain and frequency-domain multi-layer brain network, including: (1) acquiring a multi-channel EEG signal of the natural hand movement; (2) preprocessing the multi-channel EEG signal, and extracting a δ wave, a θ wave, a α wave, a β wave, and a γ wave at each time point; (3) constructing a time-domain multi-layer brain network using a wSAR model; (4) calculating the frequency-domain multi-layer brain network using the phase-amplitude coupling; (5) combining the time-domain multi-layer brain network and the frequency-domain multi-layer brain network, and performing standardization; and (6) calculating metrics of the time-domain and frequency-domain multi-layer brain network and a super-adjacency matrix of a decomposed time-domain and frequency-domain multi-layer brain network, inputting the same to a two-layer graph convolutional network (GCN), and fusing manual, shallow, and deep features for analysis.

A system and a method provide closed-loop sedation, anesthesia, or analgesia by monitoring EEG and automatically adjusting delivery of sedative, anesthetic, and / or analgesic drugs to maintain a desired or predetermined level of cortical at all echelons of care. The system and the method further monitor a subject's cortical activity to detect occurrence of burst suppression which can be indicative of unsafe depth of anesthesia or sedation. Further, the system and the method provide for alteration or cessation of administration of anesthesia or sedation based on the occurrence of burst suppression to mitigate harm to the subject.

A system for the real-time delivery of personalized motivational content based on biometric information; the system includes: a biometric acquisition module configured to capture a variety of physiological signals from a user, wherein the physiological signals include at least heart rate variability, electrodermal activity, facial expressions and electroencephalographic (EEG) signals; a preprocessing module that is operationally coupled with the biometric acquisition module, wherein the preprocessing module is configured to remove noise, normalize and extract signal features from the physiological signals in real time; a multimodal biometric fusion engine configured to temporally align and synchronize the extracted features across signal modalities using dynamic time distortion and confidence-weighted interpolation; a motivational state inference model with a hybrid neural architecture comprising a Convolutional Neural Network (CNN) for spatial pattern recognition and a Recurrent Neural Network (RNN) for temporal sequence modeling, wherein the inference model is configured to output a motivational input score and an affective state classification; an engine for recommending motivational content, configured to select and prioritize content from a content repository based on motivational uptake score, user profile metadata, contextual signals including time of day and geolocation, and historical content effectiveness profiles; and a content delivery subsystem comprising one or more output modalities selected from an acoustic actuator, a visual display, a haptic actuator or an environmental controller, wherein the content delivery subsystem is capable of presenting the selected motivational content in a modality that is dynamically adapted to the user's current psychophysiological state.

A symbolic neuroadaptive control system is disclosed for real-time arbitration, consent, and ethical modulation of artificial intelligence agents operating in wearable computing environments. The system integrates multimodal biometric telemetry—including high-resolution EEG signals—with a symbolic kernel that performs logic-driven arbitration over cognitive, emotional, and ethical states. Using Coq-verified invariants and zero-knowledge biometric consent tokens, the system constructs a deterministic symbolic execution graph, gating AI outputs based on internal user states such as trauma, stress, or intentionality. Unlike conventional black-box BCI models, the invention routes EEG-inferred affective-symbolic tokens through a formal ethics layer that enforces real-time interrupt control, utility bounding, and trust verification. The kernel enables AGI systems to defer or modify behavior based on user-state alignment, granting sovereign agency over all downstream actions. This neuro-symbolic architecture redefines the interface between human cognition and intelligent machines, enabling emotionally conscious, morally verifiable, and symbolically transparent AI governance in dynamic, high-stakes contexts.The present invention relates to artificial intelligence and neurotechnology, specifically to a real-time, neuro-symbolic operating system kernel that converts electroencephalography (EEG) signals into structured symbolic data for use in emotional cognition, ethical prioritization, autonomous agent dispatch, and real-time telecommunications routing. The invention bridges brain-computer interface (BCI) inputs with symbolic AI architectures to enable ethically aligned machine response during cognitively or emotionally intense events.

Systems and methods for controlling anesthetic-mediated unconsciousness in a patient are disclosed. An electroencephalogram (EEG) of a patient under a GABAergic-anesthetic may be recorded using EEG electrodes. Unconsciousness maintained by GABAergic anesthetics, such as propofol, propanidid, and / or sevoflurane, is characterized by slow-delta oscillations (0.3-4 Hz) and alpha oscillations (8-14 Hz) that may be viewed in an electroencephalogram (EEG). An alpha modulation index and a slow modulation index may be computed to characterize the level of unconsciousness of the patient and set a desired level of unconsciousness. The system may automatically or manually adjust the infusion rate of the anesthetic to achieve the desired level of unconsciousness.

The invention discloses a learner cognitive state recognition method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining electroencephalogram data of a learner in a plurality of learning behavior processes; dividing data of each electrode channel in the preprocessed electroencephalogram data into frequency band data of a plurality of different frequency bands, segmenting the plurality of frequency band data corresponding to each electrode channel according to a preset time window to obtain a plurality of time sequence frequency band data, and extracting a plurality of signal features from all the time sequence frequency band data; constructing four-dimensional time sequence feature data based on the extracted signal features; performing fusion feature representation on the four-dimensional time sequence feature data to obtain a fusion feature vector; and inputting the fusion feature vector into a pre-trained cognitive state recognition model to obtain a cognitive state recognition result of the learner. According to the learner cognitive state recognition method and device, the electronic equipment and the storage medium, the cognitive state of the learner can be accurately recognized.

Provided herein are apparatuses, systems and methods for non-invasively assessing synaptic dysfunction in a patient with Alzheimer's Disease. Transcranial magnetic stimulation (TMS) is applied to at least one brain region of the patient. A plurality of TMS evoked potentials generated in response to the TMS applied to the at least one brain region of the patient is recorded with a plurality of electroencephalography (EEG) electrodes. At least one characteristic of the plurality of TMS evoked potentials is analyzed to assess synaptic dysfunction in the patient. An indication of the synaptic dysfunction assessment of the patient is output.

The invention relates to the technical field of electroencephalogram monitoring, in particular to a non-contact electroencephalogram monitoring method and system, and the method comprises the steps: obtaining a corresponding head image of a target user at a current moment; determining a corresponding head pose of the target user at the current moment based on the head image corresponding to the current moment; based on the head pose at the current moment and the head pose corresponding to the previous moment, the pose of an electroencephalogram cap is determined, the electroencephalogram cap comprises a plurality of brain electrodes, and the brain electrodes monitor electroencephalogram data of the target user under the pose corresponding to the electroencephalogram cap. The problems that in the non-contact electroencephalogram collecting process, when the head moves, the collecting position deviates, and signals are not stable can be solved.

The invention relates to the technical field of fatigue detection, in particular to a pilot pre-post fatigue accumulation prediction method based on a GCN-T algorithm, and the method comprises the steps: obtaining an original EEG signal of a pilot based on a Stroop fatigue stimulation experiment before the pilot is on the post; the method comprises the following steps: preprocessing an original EEG signal to extract EEG data, inputting the EEG data into a GCN-Transform model for classification, and obtaining the state of a pilot before the pilot is on duty; wherein the state comprises a normal state, general fatigue or severe fatigue; according to the method, the fatigue state of the pilot is predicted by introducing short-time fatigue stimulation and combining the EEG, and the future fatigue risk of the pilot can be evaluated under the condition that normal work tasks of the pilot are not interfered.

A system and method for integrated magnetic resonance imaging (MRI) and electroencephalogram (EEG) is described. The system includes an integrated radiofrequency (RF)-EEG cap that includes an EEG cap comprising a first layer of material containing a first plurality of holes that provide placeholders configured to receive EEG electrodes and an RF cap comprising a second layer of material containing a second plurality of holes and a plurality of RF coils fixed onto the second layer of material. The system also includes one or more fasteners configured to co-register the second plurality of holes to the first plurality of holes and to removably secure the RF cap to the EEG cap.

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 present invention relates to an EEG analysis method and apparatus for diagnosing depression, and the method includes the steps of: generating sounds so that repetitive acoustic stimuli may be transferred to a subject; acquiring EEG signals measured from the subject in synchronization with the acoustic stimuli; calculating P300 latency using the acquired EEG signals; and providing depression diagnosis information for the subject on the basis of the calculated P300 latency.

A node-edge symbolic consent kernel (NESCK) provides a computing architecture in which every instruction is gated by a verifiable human-intent signal and an ethical-predicate chain prior to execution. The system integrates a biometric-sensing front-end (EEG / GSR / facial micro-affect), a symbolic arbitration engine that transforms bio-intent data into consent tokens, and a cryptographically bonded node-edge ledger that records execution lineage, revocation, and audit proofs. Each node represents an executable state bound to a human consent fingerprint, while each edge encodes the ethical transition rules authorizing propagation through the network. At runtime, the kernel evaluates symbolic predicates, verifies zero-knowledge proofs of consent, and allows or halts instruction dispatch. The framework operates across devices, edge nodes, and cloud layers, enabling real-time lawful AI behavior, revocable autonomy, and tamper-proof moral audit trails. Embodiments span neuroadaptive wearables, autonomous vehicles, robotics controllers, and sovereign AI systems requiring continuous consent and transparent accountability.

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.

The present invention relates to a system and method for assessing and training the quality of attentional awareness and control of an individual. The individual's attention is monitored using a neurophysiological system such as EEG while using a computer system and display that provides signals that allow the correlation of behavioral measures of attention with neurophysiological measures. The combination of those signals is a novel, accurate and reliable system for assessing any individual's true attention capabilities. This system and methods can also be modified to diagnose and / or to treat anxiety and PTSD by desensitization the user to emotion-triggering stimulus thereby diminishing the effects of anxiety on capturing and controlling attention.

The embodiment of the invention provides a cross-subject emotion recognition method and system based on a hybrid expert model. The method comprises the following steps: splitting an acquired multi-channel electroencephalogram into a plurality of local electroencephalograms according to a brain region structure so as to reduce the complexity of cross-subject identification; the local electroencephalogram is input into a hybrid expert model, and the hybrid expert model comprises a brain circuit router and a hybrid graph expert block constructed by a space-time diagram convolutional network; and in the hybrid expert model, the brain circuit distributes the local electroencephalogram to the corresponding image expert block for processing, generalization emotion recognition is carried out on the distributed local electroencephalogram of the brain circuit through the image expert block, and an emotion recognition result is output. According to the embodiment of the invention, the MoGE mixed graph expert model is provided, and the EEG signal is decomposed by using a mixed expert architecture. The complex multi-channel electroencephalogram is split into a simple partial electroencephalogram. A complex EEG signal can be decomposed into a plurality of relatively simple parts and the relatively simple parts are respectively solved, so that cross-subject emotion recognition is realized.

The present invention discloses an automatic labeling system for abnormal bands in epileptic EEG images, which relates to the technical field of epileptic EEG. The system performs frequency domain analysis and time domain feature extraction on EEG signals through Fourier transform and convolution operations, and can accurately identify abnormal waveforms such as sharp waves, spike waves and slow waves, thereby enhancing the system's sensitivity to subtle abnormal bands and merging highly similar abnormal bands into a complete abnormal event, avoiding redundant labeling and repeated event labeling. By calculating spectral entropy and spectral entropy difference, setting a reasonable threshold, and further refining it in combination with ratio difference, the system can more accurately identify and label different epileptic stages. The system optimizes labeling through multi-dimensional feature differences such as spectral entropy difference and ratio difference of different bands, and can more accurately distinguish between onset, precursors and normal in different stages of epilepsy. This multi-level labeling method effectively improves the predictive ability of epilepsy.

A wearable real-time stroke detector apparatus can be configured to be coupled to one or more EEG skin electrodes located on a subject for performing real-time stroke detection. This can include signal processor circuitry. Power spectral density (PSD) circuitry receive an acquired EEG signal acquired via a channel coupled to a skin electrode of the one or more EEG skin electrodes. The PSD circuitry can compute a monitored PSD EEG signal using the acquired EEG signal. Baseline-adjustment circuitry form a baseline-adjusted monitored PSD EEG signal using the monitored PSD EEG signal and the baseline PSD EEG signal. Classifier circuitry can use a trained model to classify a temporal shift in the baseline-adjusted monitored PSD EEG signal, over a specified range of frequencies, to produce an alert indicating Detected Stroke based on the classified temporal shift as determined by the classifier circuitry using the trained model.

The invention discloses a phase synchronization sequence parameter epileptic seizure prediction method and equipment based on KL divergence enhancement, and belongs to the field of electroencephalogram epileptic seizure prediction. After filtering preprocessing is carried out on epilepsy EEG data, an instantaneous phase of each channel is extracted based on Hilbert transformation, a phase locking value is calculated, and a phase synchronization matrix is obtained. According to the method, PLV values of all channel pairs in the network are extracted, arithmetic averaging is carried out on the values, global synchronization sequence parameters are calculated, and phase consistency of brain intervals is visually reflected. Then, empirical distribution and von Mises distribution are constructed to obtain a KL divergence matrix, and a global KL divergence synchronization index is calculated; experimental results show that the amplification and significance of the synchronous sequence parameter based on the KL divergence in the same stage are more prominent, higher sensitivity is shown in the recognition of the mutation of the synchronous mode, and an important reference basis can be provided for subsequent diagnosis and treatment.

The invention provides an electroencephalogram abnormal signal detection method based on step-by-step identification and multi-agent decision, and relates to the technical field of biomedical signal processing.The method comprises the steps that electroencephalogram signal data of an epilepsy patient are collected, and the data are analyzed according to a preset channel sequence and cut into a plurality of signal segments with the same length; extracting a multi-dimensional feature from each signal segment; inputting the time domain feature, the frequency domain feature and the inter-channel synchronization feature of each signal segment into an isolated forest model, and screening based on an abnormal proportion threshold to obtain at least one potential abnormal segment; based on the depth scattering feature, the wavelet transform feature, the time domain feature and the frequency domain feature of each potential abnormal segment, executing a multi-agent integration decision on each potential abnormal segment to obtain a comprehensive abnormal score corresponding to each potential abnormal segment; and determining an abnormal signal in the electroencephalogram signal data based on the comprehensive abnormal score corresponding to each potential abnormal segment. According to the method, the false alarm rate of electroencephalogram abnormal signal detection is remarkably reduced.

The present invention relates to techniques for detection of depression cues in EEG signals using power spectral density-based features for deep learning. In an embodiment, a system for detecting depression in a person may comprise an Electroencephalogram (EEG) electrode array attached to the person, the EEG electrode array comprising at least ten electrodes adapted to read EEG signals, an amplifier, a filter, and an Analog to Digital Convert to digitize the read EEG signals, and computing circuitry comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor to perform: processing on the digitized read EEG signals to detect signal patterns indicative of depression in the person, and outputting a prediction indicating a likelihood of depression in the person based on the deep learning processing.

A system which can be used for carrying out a method for determining an efficacy of a transcranial magnetic stimulation (TMS) treatment, including:delivering TMS to a patient using a TMS protocol in a TMS treatment session;recording EEG signals before and after the delivering during the treatment session;recording EEG signals after completion of the treatment session;analyzing the recorded EEG signals to generate values of one or more EEG parameters;determining an acute effect and an intermediate effect of the TMS protocol on a brain state of the patient based on a change in values of the one or more EEG parameters;determining an efficacy of the TMS protocol based on the determined acute effect and the intermediate effect.

The present invention relates to a computer-implemented method for controlling the operation of a robotic prothesis. The method is carried out by receiving and processing EMG and EEG signals received from a user and sending said signals to a nanocomputer, wherein artificial intelligence and specific algorithms interpret the received signals and activate different servomotors, thus allowing the robotic prothesis to move. Furthermore, additional sensors for protecting the user, an inertial measurement unit (IMU), a virtual assistant and a failure diagnosis and loading system are included. The method is managed via a computer program that is loaded into the nanocomputer.

A method of predicting a seizure occurrence or a surgical outcome includes monitoring a patient using an electroencephalography (EEG) system, including recording EEG data that indicates a seizure, and extracting a peri-ictal segment of the EEG data that includes a pre-ictal period immediately preceding the seizure, or a post-ictal period immediately following the seizure. The method also includes processing the peri-ictal segment, including generating an electrode-wise power spectral density (PSD) feature across a frequency band defined by at least one of a delta frequency range, a theta frequency range, an alpha frequency range, a beta frequency range, and a gamma frequency range. The method also includes inputting the PSD feature into a model, wherein the model predicts a seizure occurrence or a surgical outcome of the patient.

The embodiment of the invention provides a multi-regularization self-supervised training method and system for an emotion recognition model. The method comprises the following steps: inputting an electroencephalogram sample into a first emotion recognition model based on masking channel modeling of time spectrum transformation and a second emotion recognition model based on emotion contrast learning; performing emotion recognition on the electroencephalogram sample by using a first emotion recognition model and a second emotion recognition model to obtain a predicted emotion classification result and emotion classification loss; determining a logic value of the electroencephalogram sample through an encoder of the first emotion recognition model and a classifier head, and determining an emotion feature and regularization loss of the electroencephalogram sample through an encoder of the second emotion recognition model and a projection head; and performing self-supervision training based on the emotion classification loss and the regularization loss. According to the multi-regularization self-supervised training in the embodiment of the invention, time-frequency spectrum transformation masking channel modeling and emotion comparison learning pre-training are adopted respectively, so that more accurate emotion information can be captured.

A wearable device for recording biometric information from a person's brain can be embodied in a pair of smart headphones with electrodes or in a biauricular headset with electrodes. This wearable device can position a plurality of electrodes on a person's head at a subset of the following MCN electrode placement sites: CP1, CP2, CP3, CP4, CP5, CP6, CPz, FC1, FC2, FC3, FC4, FC5, FC6, FCz, O1, O2, Oz, P7, P8, PO7, PO8, TP7 and TP8.

The invention provides an artificial cochlea objective debugging method and system based on wide-spectrum natural sound evoked electroencephalography, and the method comprises the steps: collecting an auditory electroencephalogram of a tested person under the stimulation of wide-spectrum natural scale sound synthesized by a plurality of vocoders, and carrying out the preprocessing of the auditory electroencephalogram, and obtaining an auditory electroencephalogram data set; training the deep learning network model by using data in the auditory electroencephalogram data set to obtain a trained deep learning network model; the deep learning network model is provided with two convolution layers for extracting features including time frequency and channel information of an auditory electroencephalogram, and is provided with a full connection layer for classification to obtain scale sound sensing electroencephalogram decoding and prediction accuracy; and quantizing different vocoder algorithm performances according to the obtained scale sound sensing electroencephalogram decoding and prediction accuracy, and objectively debugging the artificial cochlea based on the quantized different vocoder algorithm performances.

An integrated EEG-fNIRS neurofeedback system for interacting with participant's brain activity includes EEG electrodes, fNIRS detectors, at least one information receiver, a computation module, and a report generator. The EEG electrodes collect EEG signals. The fNIRS detectors collect fNIRS signals. The at least one information receiver receives and processes the collected EEG and fNIRS signals. The computation module executes an EEG and fNIRS signal processing pipeline with the EEG electrodes, the fNIRS detectors, and the information receiver. The computation module is further configured to: calculate score information based on received EEG and fNIRS signals; select a minimum score from the calculated score information; and discard an alternative score that is not selected as the minimum score, so as to enable the computation module to choose a single representative score for the shared target objective from both EEG and fNIRS signals. The report generator provides a report of the selection.

The invention is suitable for the field of medical technology, and provides a cerebral apoplexy monitoring method and system based on a multi-modal brain-computer interface, and the system comprises a multi-modal physiological data acquisition module, a data denoising and feature extraction module, a multi-modal data fusion module, an abnormal mode recognition module, and a risk signal alarm module. The system can be combined with different types of physiological signals to provide a comprehensive and real-time monitoring platform so as to support early recognition and timely intervention of cerebral apoplexy. The system can effectively capture the electrical activity and blood flow change of the brain of a patient by monitoring electroencephalogram and near infrared spectrum data in real time, so that deep physiological state analysis is provided for doctors; by simultaneously acquiring the electroencephalogram signal and the blood flow change data, the system not only can analyze the neural activity of the brain, but also can evaluate the blood supply condition of the brain. By means of the comprehensive monitoring, a doctor can judge the health condition of the patient more accurately on the basis of comprehensively considering the brain function and the blood flow state.

The invention discloses a system and a method for reducing power consumption of electroencephalogram acquisition equipment. The system comprises a visual stimulation module, a label synchronization module, an electroencephalogram signal classification module and an upper computer module, the system further comprises a low-power-consumption electroencephalogram acquisition module, the low-power-consumption electroencephalogram acquisition module completes acquisition and wireless transmission of electroencephalogram signals in a multi-core awakening working mode, and in the multi-core awakening working mode, time parameters including wireless communication connection intervals are calculated and set, so that the real-time performance and integrity of data are ensured; wherein the low-power-consumption electroencephalogram acquisition module is composed of an electroencephalogram acquisition electrode unit, an analog acquisition front-end unit, a master control and wireless transmission chip unit, a first clock, a second clock and a power management unit, and the master control and wireless transmission chip unit is composed of a main processor, a radio frequency processor and a coprocessor. The power consumption of the wearable electroencephalogram machine is reduced, and the endurance of the electroencephalogram machine under a small-capacity battery is enhanced.