129 results about "Eeg electrodes" patented technology

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.

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 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 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.

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.

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.

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.

Systems and methods for electroencephalography headband apparatuses including a strap member, an amplifier device, and electrode assemblies each including a capsule receiving structure having a detent feature configured to resist rotation of a gel capsule positioned there within, a stabilization structure having a central attachment section and a plurality of leg members positioned radially outward from a central axis of the electrode assembly, and a gel delivery structure configured to be positioned at least partially within the capsule receiving structure and to rotatably attach to the stabilization structure such that the gel delivery structure may be able to rotate independently of the stabilization structure. The gel delivery structure may include a plurality of teeth members configured to engage with and lacerate a lower surface of the gel capsule and a plurality of gel delivery channels configured to deliver gel from the gel capsule to patient scalp.

Systems and methods for transmitting brainwaves or control signals via body communication from the brain to the ends of the body to control or activate body parts or even external devices. An EEG coupler / transceiver couples to a person's scalp, comprising an EEG electrode for receiving a brainwave from the person, an EEG body communication coupler, and an EEG antenna for transmitting a signal via the EEG body communication coupler. An activator coupler / transceiver couples to the person's body to stimulate a muscle, comprising a muscle activator, an activator body communication coupler, and an activator antenna for receiving the signal via the activator body communication coupler.

This invention relates to the field of nursing care products, and more particularly to a nursing pillow for neurological data acquisition, comprising a pillow, a central pillow pad, a pad block, and a support pad; the pillow consists of a support block and two side pillow pads; a central pillow pad supporting the patient's head is connected to the support block; the central pillow pad is located between the two side pillow pads; each side pillow pad has a pad block; and each pad block has a support pad installed. This invention stores the EEG cable bundle within the cable groove of the support pad, while leaving a certain length allowance to allow the patient's head to move within a limited range, avoiding the problem of tangled EEG cable bundles and the patient pulling on the cable bundles during movement. By using the two support pads to shield the patient's head, when the patient has an attack, if their hands approach their head, they will be blocked by the support pads, preventing them from touching the EEG electrodes and cable bundles, thus avoiding interference with the use of the EEG.

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.

An electrocorticographic interface device includes a flexible thin-film electrode array formed on a biocompatible substrate such as polyimide, the electrode array having a plurality of recording electrodes and interconnect lines patterned on the biocompatible substrate, and the flexible thin-film electrode array further includes an array of perfusion holes distributed across the biocompatible substrate.

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.

The system according to the embodiment aims to accurately record the contents of dreams and share them with others. [Solution] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, a consent unit, and a sharing unit. The collection unit collects EEG data. The analysis unit analyzes the EEG data collected by the collection unit. The generation unit generates VR video based on the dream content identified by the analysis unit. The consent unit obtains the user's consent. The sharing unit shares the dream content with a third party based on the consent obtained by the consent unit.

The present disclosure relates to Sigma-1 receptor agonists, and their target engagement and capacity to correct multiple EEG biomarkers of cortical dysfunction. The present disclosure further provides Precision Medicine or personalized therapy of using a Sigma-1 receptor agonist in treating neurodevelopmental and / or neurodegenerative disorder through engaging with EEG biological pathways. Also provided are kits for practicing the methods.

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.

Systems and methods for detection of stroke and its types, comprising: electrodes, on an EEG headset (101), placed to record EEG signals; a client-side signal processing engine configured to: compute power for each of signals; segregate processed signal, from each of said electrodes, into five baskets, by processing signals from each of said electrodes such that there is a Delta basket, a Theta basket, an Alpha basket, a Beta basket extract features from a frequency component of said transformed signals in order to obtain stroke ratios; receive, as a first output, a first set of processed signals with power ratings for determination of a stroke incident as a function of power rating ratios; receive, as a second output, a second set of processed signals with relative powers for determination of a type of stroke as a function of said first relative power (RDP) and said second relative power (RAP).

Techniques for automatically detecting a seizure of a patient are presented. The techniques include: obtaining patient EEG data, where the patient EEG data represents an EEG of the patient for a plurality of channels, each channel representing a respective location in or on a brain of the patient; evaluating a tendency to act as a sink for each channel; determining, for each tendency to act as a sink, a respective energy distribution according to frequency; assessing an entropy of each energy distribution according to frequency; measuring, from at least one of the plurality of entropy quantifications, at least one sink tendency entropy drop value; identifying, based on the sink tendency entropy drop value, a presence of a patient seizure proximate to a time of a sink tendency entropy drop; and outputting an indication of the patient seizure.

A neurofeedback system includes an EEG apparatus, a presentation apparatus and a controller. The controller is configured to: (i) cause the presentation apparatus to display an overlaid image to the user that comprises a first image flickering at a first frequency and a second image flickering at a second frequency different than the first frequency, the first image being an affective distractor stimulus image and the second image being a task-relevant stimulus image, (ii) receive from the EEG apparatus a number of first steady-state visual evoked potential (SSVEP) signals generated in response the first image of the overlaid image and a number of second SSVEP signals generated in response the second image of the overlaid image, and (iii) calculate feedback indicative of how much attention of the was user allocated to the task-relevant stimulus image versus how much attention of the user was allocated to the affective distractor stimulus image.