220 results about "Eeg electrodes" patented technology

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on motivational uptake score, user profile metadata, contextual signals including time of day and geolocation, and historical content effectiveness profiles; and a content delivery subsystem comprising one or more output modalities selected from an acoustic actuator, a visual display, a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.

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.

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 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.

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 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 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 real-time self-adaptive electroencephalogram artifact suppression and enhancement embedded system. The system aims at overcoming the limitation of an existing portable EEG device in the aspect of artifact processing, various artifacts in EEG signals are recognized and removed in a low-power-consumption, high-precision and real-time mode, and meanwhile the quality of effective EEG signals is optimized and enhanced. One key innovation of the invention lies in breaking through the limitation of single EEG signal processing, and accurate separation and correction of artifacts are carried out through the assistance of EOG and EMG. For example, the EOG signal is used for assisting eye movement artifact removal through synchronous correlation analysis and an adaptive regression filter; the EMG signal is used for assisting myoelectricity artifact correction, and suppression is carried out through spectral analysis and dynamic adjustment of filter parameters. In addition, the system also has the functions of real-time detection and repair of electrode artifacts. According to the system, high-quality EEG signals close to offline processing are provided on portable embedded equipment with limited resources, and the system has universality and high-precision application potential and can be widely applied to a plurality of frontier fields such as medical diagnosis, brain-computer interface (BCI), cognitive ability enhancement, neurological rehabilitation training, sleep disorder monitoring and human-computer interaction.

The invention discloses an emotion recognition method based on spatio-temporal multi-scale attention convolutional neural network, which comprises: collecting EEG data of subjects for preprocessing to obtain EEG data containing spatial dimension and temporal dimension; constructing a lightweight convolutional neural network including two-stream spatio-temporal feature construction layer, hybrid attention mechanism layer, high-order fusion layer and classification layer; wherein the two-stream spatio-temporal feature construction layer comprises a temporal feature extraction module and a parallel spatial feature extraction module; the high-order fusion layer is used to re-learn from the learned global convolution kernel to the representation of the local hemisphere convolution kernel; the trained lightweight convolutional neural network is used to identify EEG data, and the emotion recognition results of the subjects are obtained. By constructing a lightweight model with fewer parameters, the accuracy and efficiency of EEG-driven emotion recognition are improved.

The invention relates to the technical field of earphone control, and discloses a multi-scene adaptive wireless earphone dynamic control method, which comprises the following steps: identifying environmental noise through phonon entangled state density and phase coherence length by adopting a quantum sensor array; physiological signals of a user are collected through the anti-interference electroencephalogram electrode, and the auditory state is analyzed; a millimeter wave radar is used to construct an environmental acoustic model, and quantum-classical hybrid calculation is combined to realize rapid sound field simulation; establishing a multi-objective optimization function to dynamically adjust noise reduction, equalization and spatial sound effect parameters; and realizing smooth transition control based on scene change prediction. According to the invention, the control accuracy of the wireless earphone during multi-scene adaptation can be improved.

Described are methods and a device for determining a valid intrinsic alpha frequency. Described herein are methods and a device for determining the appropriate intrinsic alpha frequency (IAF) to be applied for neuro-EEG synchronization therapy using alternating magnetic fields to gently “tune” the brain and affect the mood, focus and cognition of subjects. Methods and a device described herein use an algorithm to quantitatively analyze EEG recordings to determine if recorded EEG frequencies are valid, and if necessary, requiring additional recordings and analysis until a valid EEG is found. Methods and devices described herein can be utilized to calculate the intrinsic frequency of other EEG bands, including the Theta, Beta Gamma and Delta bands.

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.

The device (1) is used to determine insomnia in a patient (8). It has a sensor unit (2) for recording at least one EEG measurement signal of a brain wave of the patient (8) and a control and evaluation unit (3) for evaluating the recorded EEG measurement signal. The control and evaluation unit (3) is configured to carry out an objective assessment routine and in doing so, for the subsequent recording of the EEG measurement signal, to give the patient (8) a stipulation to open his eyes for the duration of an eyes-open period and to close his eyes for the duration of an eyes-closed period, and then to initiate the recording of the EEG measurement signal during at least one eyes-open period and during at least one eyes-closed period. The control and evaluation unit (3) is further configured to perform an evaluation of the EEG measurement signal thus recorded and in doing so to extract from the EEG measurement signal at least one first partial measurement signal from the at least one eyes-open period and at least one second partial measurement signal from the at least one eyes-closed period, and to decide on the presence of insomnia if the at least one second partial measurement signal from the at least one eyes-closed period has a lower proportion of α waves than in a healthy individual.

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.

This invention presents a system using an alternative method to quantifying levels of substances from the EEG data using a non-linear equation based on the same essential features of previous disclosures. The essential features consist of predetermined frequency bands, the amalgamated ratio and a regression formula: (Formula (I)) where Y is the EEG- based substance level quantification.

There is provided a computer implemented method of diagnosing a medical state associated with a neuro-psychiatric disorder in a subject, comprising: receiving a plurality of EEG datasets, each respective EEG dataset from a respective EEG electrode of a plurality of EEG electrodes monitoring a head of the subject, clustering the plurality of EEG datasets into a plurality of clusters, computing a p-adic representation of the plurality of clusters, extracting a quantum potential value from p-adic representation of the plurality of clusters, and diagnosing the medical state associated with the neuro-psychiatric disorder according to the quantum potential relative to a threshold that separates between presence of the medical state and non-presence of the medical state.

Embodiments of a system for controlling an object using brainwaves are disclosed. The system includes a set of EEG electrodes configured to be positioned on a head of a user and to collect EEG signals. The system further includes one or more computer readable storage mediums storing a framework configured to execute an extensible architecture through which EEG signals are interpreted for control of the object. The framework includes an EEG device plugin associated with the set of EEG electrodes and configured to extract the EEG signals from the set of EEG electrodes. The framework also includes an interpreter plugin configured to convert the EEG signals extracted by the EEG device plugin into a command. Further, the framework includes an object control plugin configured to access the command through an extension point of the interpreter plugin and to execute the command to control the object.

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.

An example system includes a plurality of electrodes; sensing circuitry configured to: generate, based on sensed electrical signals, one or more electroencephalography (EEG) signals; and processing circuitry configured to: receive one or more EEG signals generated during a first period of time; determine, based on at least one particular feature of the one or more EEG signals generated during the first period of time, an initial indication of a cranial acute health event during the first period of time; receive, from the sensing circuitry, one or more EEG signals generated during a second period of time; determine whether a difference between the at least one particular feature of the one or more EEG signals generated during the first period of time and at least one corresponding particular feature of the one or more EEG signals generated during the second period of time satisfies a cranial acute health event mimic threshold.

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.