690 results about "Electro encephalogram" patented technology

The present invention discloses a dyskinesia rehabilitation training method and system based on electroencephalogram signal recognition; the method includes: the convolutional neural network is used to recognize the electroencephalogram signal, analyze the patient's motor intention, convert the motor intention into a control instruction for the exoskeleton device, drive the patient's limb movement by controlling the movement of the exoskeleton device, and assist the patient to complete the movement disorder rehabilitation training. In this method, the self-attention mechanism and self-distillation training were used to establish an electroencephalogram signal recognition model, analyzing the patient's motor intention to help motor nerve remodeling, which broke the passive and single problem of traditional rehabilitation methods, realized active rehabilitation of patients, and significantly improved the rehabilitation efficacy.

The invention relates to the technical field of brain-computer interfaces, in particular to an electroencephalogram signal amplification method, device and equipment based on topological gradient field coding. The method comprises the following steps: acquiring a first data set corresponding to a scalp electroencephalogram of a testee through electroencephalogram acquisition equipment, and performing Hilbert transform to extract phase information so as to calculate a topological gradient field; key phase features meeting the energy concentration ratio feature and the duration feature are extracted from the topological gradient field, so that the key phase features with the energy concentration ratio threshold value larger than a preset value and the duration exceeding a time threshold value are screened out; inputting the key phase features to a pre-constructed generative adversarial network, and generating an enhanced phase field by a generator of the generative adversarial network through adversarial training; and combining the enhanced phase field with the low-frequency amplitude component, subjected to wavelet decomposition, of the first data set to construct a complex field, simulating spatio-temporal evolution of the complex field based on a nonlinear dynamic model, and generating a finally enhanced and amplified electroencephalogram signal. And efficient characterization of electroencephalogram phase information is realized.

The invention discloses an emotion recognition method based on a space-time multi-scale attention convolutional neural network, and the method comprises the steps: collecting electroencephalogram data of a subject, and carrying out the preprocessing of the electroencephalogram data, so as to obtain electroencephalogram (EEG) data containing a space dimension and a time dimension; constructing a lightweight convolutional neural network comprising a double-flow spatial-temporal feature construction layer, a mixed attention mechanism layer, a high-order fusion layer and a classification layer; wherein the double-flow spatial-temporal feature construction layer comprises a temporal feature extraction module and a parallel spatial feature extraction module; the mixed attention mechanism layer combines a channel attention mechanism, a space attention mechanism and a self-attention mechanism; the high-order fusion layer is used for carrying out re-learning from the learned global convolution kernel to the representation of the local hemisphere convolution kernel; and recognizing the EEG data by using the trained lightweight convolutional neural network to obtain an emotion recognition result of the subject. The precision and efficiency of emotion recognition driven by electroencephalogram signals are improved by constructing a lightweight model with a small number of parameters.

The invention discloses an emotion recognition method based on electroencephalogram (EEG) and expression data, which combines fusion analysis of electroencephalogram (EEG) signals and facial expression data, and provides an innovative solution for the complexity and accuracy of emotion recognition. The method comprises the following specific implementation steps: 1, finding an optimal frame in an expression video by using multi-instance learning, extracting global visual features by adopting Swin Transform, and realizing dimension reduction and balance representation of cross-modal information through a cross attention mechanism; 2, preprocessing the electroencephalogram signal by using band-pass filtering and a fast ICA algorithm, and extracting effective features by using PSD; and 3, designing a fusion module based on Transform, combining the characteristics of the two modes, and giving full play to the complementary advantage of the modes. Experimental results show that 94.85% of classification accuracy is achieved in four types of emotion classification tasks, and compared with an existing method, the accuracy is remarkably improved.

The invention relates to an epileptic seizure detection method and system based on self-attention mechanism and GRU-LSTM fusion, and the method comprises the following steps: (1) carrying out the preprocessing of original electroencephalogram signal data, and extracting time domain and nonlinear features; (2) randomly dividing the data set into a training set, a test set and a verification set; (3) constructing a deep learning model fusing a self-attention mechanism, a gating circulation unit and a long short-term memory network; (4) extracting fusion features from the trained deep learning model, and inputting the fusion features into a support vector machine classifier to perform epileptic seizure and non-seizure classification; and (5) outputting a classification result through multi-modal feature fusion, long and short term dependence modeling and adaptive feature selection. The method has the advantages that a self-attention mechanism, a gating circulation unit, a long-short-term memory network and a support vector machine classifier are combined, deep features of electroencephalogram signals are extracted through a multi-stage processing flow, and finally epileptic seizure and non-seizure classification is carried out.

The invention provides a high-quality 3D Mesh reconstruction method based on brain EEG (electroencephalogram) signals. The electroencephalogram signals can be quickly and efficiently converted into a high-fidelity three-dimensional mesh model. Firstly, an EEG encoder extracts time domain and frequency domain features from an EEG signal to enhance the representation capability of the signal. The time-frequency embedding is then input to a two-stage image generation module guided by the EEG, generating a high-quality two-dimensional image. And finally, through a multi-view diffusion model and a sparse view reconstruction technology, the generated two-dimensional image is used as input, and reconstruction of the three-dimensional grid is rapidly completed. According to the scheme, the technical problem of converting the EEG signal into the three-dimensional object is effectively solved, an important breakthrough is made in the fields of electroencephalogram signal processing and three-dimensional reconstruction, and a new view angle and a new tool can be provided for research in the fields of acknowledging science, psychology and neuroscience.

The invention discloses an intelligent brain-controlled muscle electrical stimulation rehabilitation therapeutic apparatus, and relates to the technical field of electrical stimulation rehabilitation therapeutic apparatuses, the intelligent brain-controlled muscle electrical stimulation rehabilitation therapeutic apparatus comprises an electroencephalogram signal acquisition module, an electromyographic signal acquisition module and a mixed signal processing module, the mixed signal processing module is connected with the electroencephalogram signal acquisition module and the electromyographic signal acquisition module; the method is used for preprocessing electroencephalogram and electromyogram signals, fusing multi-modal features and classifying motion intentions. By installing the mixed signal processing module, preprocessing, multi-modal feature fusion and motion intention classification of electroencephalogram and electromyogram signals are achieved, a preprocessing unit can effectively remove ocular artifacts and power frequency noise, meanwhile, independent component analysis and a dynamic baseline calibration strategy are adopted, the signal quality is further improved, and the accuracy of motion intention classification is improved. According to the multi-mode signal processing mode, the limitation of single signal processing in the prior art is overcome, and the accuracy and effectiveness of rehabilitation treatment are improved.

The invention discloses an epileptic seizure auxiliary prediction and cognitive function evaluation system and method based on multi-modal data fusion and intelligent adaptive optimization. All the modules work cooperatively, and intelligent optimization from multi-dimensional electroencephalogram signal, motion data and physiological data collection, intelligent preprocessing and feature deep extraction and fusion to epileptic seizure prediction and cognitive function evaluation is achieved. Through the combination of neural architecture search and self-supervised comparative learning, the network structure can be automatically optimized on the basis of multi-modal data, and the ability of learning and identifying an epileptic seizure mode and a cognitive state is effectively improved. The system realizes high-accuracy epileptic seizure prediction and cognitive state evaluation through adaptive optimization, and remarkably reduces the missing report rate and the false report rate. The system has the characteristics of high efficiency, intelligence and accuracy, can provide timely early warning for epilepsy patients, provides scientific cognitive function intervention suggestions for doctors, and has wide application prospects.

The invention discloses an automatic marking system for an abnormal wave band of an epilepsy electroencephalogram image, and relates to the technical field of epilepsy electroencephalograms, the system carries out frequency domain analysis and time domain feature extraction on an electroencephalogram signal through Fourier transform and convolution operation, abnormal waveforms such as sharp waves, ratchet waves and slow waves can be accurately identified, and the accuracy of the abnormal wave band marking is improved. The sensitivity of the system to fine abnormal wavebands is enhanced, highly similar abnormal wavebands can be combined into a complete abnormal event, redundant annotation and repeated event annotation are avoided, further refinement is performed through calculation of the spectrum entropy and the spectrum entropy difference value, setting of a reasonable threshold value and combination of the ratio difference value, and the abnormal event annotation accuracy is improved. The system can identify and label different epilepsy stages more accurately, the system optimizes labeling through multi-dimensional feature differences such as frequency spectrum entropy difference and ratio difference values of different wavebands, seizure, precursor and normality of epilepsy in different stages can be distinguished more accurately, and the multi-level labeling method effectively improves the epilepsy prediction capacity.

The invention relates to a deep learning and emotion recognition technology, in particular to a two-way electroencephalogram emotion recognition method and system combining general path learning and medical priori. The method comprises the following steps: preprocessing an electroencephalogram signal, and extracting a difference entropy feature; constructing an adjacent matrix AC based on a general path and an adjacent matrix AR of a brain region divided based on medical priori; taking the difference entropy features as node features, respectively taking the adjacent matrixes AC and AR as edges, and constructing a path graph structure and a prior graph structure; performing graph convolution operation on the path graph structure and the prior graph structure to update node features according to the adjacency matrixes AC and AR; fusing the two paths of updated node features to obtain high-dimensional fusion feature matrix representation; and the electroencephalogram emotion is identified and classified through a multi-layer perceptron. According to the method, general emotion recognition features and biological prior of brain region division are effectively combined, and more comprehensive and accurate feature representation is provided for emotion recognition.

State of art techniques, need a decoder following the encoder to encode EEG signals, whose morphology is undefined. Embodiments herein disclose a method and system for a Spiking Neural Network (SNN) based low power cognitive load analysis using electroencephalogram (EEG) signal. The method receives a raw EEG signal from multichannel EEG set up, wherein each of the raw EEG signal is re-referenced and encoded into a spike train using a Light-Weight-Lossless-Decoder less-Peak-based (LWDLP) encoding. Further, the spike trains are processed by the SNN architecture using backpropagation based supervised approach, wherein the spatial information and the temporal information are learnt by the SNN in form of neuronal activity and synaptic weights. Post learning the SNN architecture applies an activation function on the neuronal activity for classifying a cognitive load level experienced by a subject from among a plurality of predefined cognitive load levels using a SNN classifier.

The invention discloses an EEG signal spine wave detection method and system based on KAN feature fusion, and the method comprises the steps: carrying out the preprocessing of electroencephalogram data, and obtaining a training set and a test set; performing parallel feature extraction to obtain multi-KAN parallel features, and performing splicing and dimension reduction to obtain mixed features; performing gating dynamic selection on the obtained mixed features to obtain fusion features of comprehensive multi-scale analysis of the EEG signals; focusing the key period features based on a double attention mechanism to obtain weight fusion features; inputting the obtained weight fusion features into a classifier, and training a prediction model based on a cross entropy loss function; evaluating the performance of the model, and outputting a classification result. The epileptic seizure risk prediction is realized by extracting the electroencephalogram heterogeneous features, and inputting the electroencephalogram heterogeneous features into the classifier after gating dynamic fusion and double attention time sequence compression. The feature expression capability is obviously improved, the focusing capability in a key period is enhanced, and the classification performance is optimized.

The invention relates to a three-dimensional spiral high-density neural electrode and a preparation method and application thereof. The three-dimensional spiral high-density neural electrode comprises a probe structure and a plurality of electrode sites. The probe structure is formed by curling a planar flexible electrode precursor, and at least one end of the probe structure is provided with a spiral outer surface of a three-dimensional spiral line structure. Electrode sites are distributed along a spiral path, have a size of 5-1000 [mu] m, and are used for contacting biological tissues. On the planar flexible electrode precursor, electrode sites are arranged on one or more straight lines forming an inclined angle alpha with the axial direction of the probe, and the spatial distribution is matched with the edge. Compared with the prior art, the method has the advantages that the constraint of a traditional wiring mode is broken through, the integration of high-density three-dimensional channels is realized under a micro size, and a new generation of solution is provided for a high-precision brain-computer interface, deep brain stimulation and a three-dimensional electroencephalogram.

The invention belongs to the technical field of signal decoding, and relates to an electroencephalogram signal decoding method and device, equipment and a medium. The method comprises the following steps: acquiring a data set of electroencephalogram signals, and obtaining a training set and a test set; constructing a teacher model, and inputting the long-length electroencephalogram signals in the training set into the teacher model for training to obtain a pre-trained teacher model; constructing a student model, respectively inputting the long-length electroencephalogram signal and the short-length electroencephalogram signal in the training set into the pre-training teacher model and the student model, and then calculating the characteristic distillation loss based on frequency mask generation distillation by taking the middle layer characteristics of the pre-training teacher model as transmitted knowledge to obtain the total loss of the student model; performing supervised training on the student model by adopting the pre-trained teacher model to obtain an optimized student model; and decoding the short-length electroencephalogram signals in the test set by adopting an optimized student model. According to the invention, the decoding precision of the short-length electroencephalogram signal can be improved.

The invention relates to the technical field of electroencephalography, in particular to a sleep staging automatic identification method and system based on multi-mode electroencephalography, and the method comprises the following steps: extracting a waveform period to construct dominant distribution, correcting label boundary positioning mutation response, unifying a time axis to form an alignment structure, clustering mutation fragments to establish an alternating relation, and identifying the sleep staging based on multi-mode electroencephalography. And identifying a main channel range to construct an automatic identification scheme. According to the method, a reference channel is positioned through area distribution difference, a boundary change section is judged in combination with a frequency band energy change trend, a channel region with dense and stable mutation points is extracted as an anchor point, time axis unified adjustment is completed according to main response starting and ending time, and an alternating fragment graph is further constructed through a cross-channel time coverage relation of the mutation points. The sequential structure coordination ability and mutation form aggregation expression efficiency among multi-source signals are improved, and accurate recognition of boundary drift and asynchronous response and dynamic extraction of steady-state rhythm in sleep stage division are achieved.

The invention discloses a multi-modal deep learning traceability method and system fusing magnetoencephalogram and electroencephalography, and relates to the technical field of artificial intelligence and neuroimage.Real magnetoencephalogram signals and electroencephalography signals are preprocessed and then input into a traceability model, the probability of occurrence of a source in a corresponding area is predicted, and the traceability of the source in the corresponding area is obtained by combining an imported source partition distance matrix. A final traceability result is obtained; the training process of the traceability model is as follows: constructing a generative adversarial network, and generating a multi-modal neural electrophysiological data set; inputting the multi-modal neural electrophysiological data set into a residual network of a double-branch structure, and performing stage hierarchical extraction and decoupling on magnetoencephalogram signals and electroencephalogram signals respectively; extracting features in different stages by using a multi-scale convolution module, fusing the extracted features, inputting the fused features into a classifier, defining a loss function, and updating trainable parameters of the traceability model; the traceability method improves the accuracy and generalization ability of traceability positioning.

The invention discloses a brain region correlation analysis system and method for autistic children. The system comprises a brain region division module used for dividing the cerebral cortex into a plurality of brain regions; the training data set construction module is used for constructing a training data set, and each training sample comprises a sensor space function connection matrix and a source space function connection matrix corresponding to the sensor space function connection matrix; the signal preprocessing module is used for acquiring a real electroencephalogram signal and calculating a real value of a corresponding sensor space function connection matrix; the deep learning mapping module is used for learning a mapping relation from the sensor space function connection matrix to the source space function connection matrix and outputting a predicted value of the source space function connection matrix; and the brain region correlation analysis module is used for calculating the correlation between the brain regions. The method can be used for accurately analyzing the correlation between the brain areas of the autism children.

The invention discloses a multi-mode postoperative child pain identification method. The method aims at improving the accuracy of child postoperative pain recognition by fusing ECG (electrocardiogram), SPO2 (blood oxygen) and EEG (electroencephalogram) multi-mode data. The method comprises the following steps of: firstly, respectively acquiring and preprocessing each modal data, and converting EEG (electroencephalogram) into two types of two-dimensional frames; then, feature extraction is automatically conducted on the data of all the modes through specific neural networks including the frequency domain flow neural network, the time domain flow neural network, the oxyhemoglobin saturation CNN-GRU neural network and the electrocardio CNN-GRU neural network; and finally, inputting the extracted features into an MLP feature fusion layer to carry out pain identification. Compared with a traditional single-mode identification method, the postoperative child pain related characteristics can be more comprehensively captured, the pain identification accuracy is effectively improved, and powerful support is provided for medical staff to accurately evaluate the postoperative child pain degree in time.

Evaluation system for obtaining and analyzing user interaction and physiological data to provide adaptive feedback, comprising: a portable multi-sensor device comprising: an electroencephalography sensor designed to measure the user's brainwave signals; and a galvanic skin response sensor designed to measure skin conductivity, which indicates the user's stress level; an interactive assembly device designed to recognize the user's assembly actions and generate corresponding assembly data; a computing device in communication with the wearable multi-sensor device and the interactive montage device, the computing device configured to receive and combine the brainwave signals, the skin conductance, and the montage data. Analyze the combined data in real time to create an estimate of the user's cognitive state and provide adaptive feedback to the user based on the cognitive state estimate via at least one output module. wherein the computing device is further configured to store the brainwave signals, the skin conductance, the compilation data, and the cognitive state estimate in a database for later retrieval and review.

The invention relates to the technical field of intelligent early warning, and discloses a multi-mode intracranial pressure intelligent early warning method and system.The method comprises the steps that an intracranial pressure waveform signal, an electroencephalogram signal and a transcranial Doppler blood flow velocity signal are synchronously collected, and a multi-mode physiological data set is obtained; extracting time domain features, frequency domain features and nonlinear features of the preprocessed multi-modal physiological data set to obtain a standardized feature parameter set; distributing dynamic weights for features in the standardized feature parameter set according to feature modal types of the standardized feature parameter set to obtain a fusion feature vector; performing intracranial pressure prediction on the patient according to the fusion feature vector after nonlinear transformation to obtain an intracranial pressure change sequence; according to the deviation condition of the intracranial pressure change sequence and a clinical preset safety threshold value, an intracranial pressure early warning signal is output; the accuracy of intracranial pressure early warning can be improved.

The invention provides a brain-computer interface closed-loop nerve regulation and control method, a storage medium, a system and a program product, and the method comprises the following steps: circularly executing the following steps according to a first period until an end condition is met: controlling transcranial magnetic stimulation equipment to run for a first time length according to a set stimulation parameter to stimulate a testee; collecting an electroencephalogram signal sequence of the testee in the target brain area; the electroencephalogram signal sequence is processed, a preset index is obtained, and the preset index is used for reflecting the active condition of the target brain area; and processing the preset index by using a deep reinforcement learning network to obtain a stimulation parameter of the next period. According to the method, the brain state is adjusted according to accurate individual characteristics in a self-adaptive closed-loop mode, and a more accurate and effective nerve regulation and control scheme can be provided.

The invention discloses a five-dimensional dynamic emotion visual chemotherapy healing method and system, and relates to the technical field of emotion visual chemotherapy healing, and the method comprises the steps: employing a multi-sensor fusion method to collect physiological data, psychological assessment questionnaire results and historical emotion data of a user, and obtaining a basic emotion feature vector of the user; mapping the emotional state of the user to a five-dimensional emotional space based on the basic emotional feature vector, and initializing a particle set; collecting multi-mode biological signals of electroencephalogram, heart rate variability, electrodermal response, body temperature and voice emotion recognition of the user, and processing to obtain a feature vector reflecting the current emotion state of the user; the geometric morphology and kinetic parameters in the particle set are dynamically adjusted, a visualization engine is used for rendering particles, a two-way feedback adjustment mechanism is constructed, particle behaviors are adjusted according to the emotional state of the user, and the emotional adjustment ability of the user is enhanced; and designing an interactive emotion regulation game.

Provided herein are systems, kits, and methods for monitoring brain activity. In some implementations, a system includes a plurality of wearable sensors having a housing with an extended, rounded shape are removably attached to the scalp of a patient and monitor electroencephalogram (EEG) signals. Approaches for instructing a user to position and active that wearable sensors are disclosed. Approaches for facilitating collection, synchronization, and processing of EEG signals are disclosed. Approaches for handing off control of the wearable sensors between portable computing devices are disclosed.

The present invention provides an early intelligent screening and warning method and system for acute stroke based on deep learning, which relates to the field of medical imaging and artificial intelligence technology, including obtaining multi-phase angiography images of the patient's brain through a pulsed dynamic imaging scheme, extracting hemodynamic characteristics using a dual-stream feature extraction network, and constructing a vascular lesion risk distribution map in combination with a vascular structure analysis model to mark potential embolism risk areas. Further, combining tissue perfusion parameters obtained by multispectral imaging and brain tissue function parameters obtained by electroencephalogram signals, an adaptive threshold segmentation model is used to identify the ischemic penumbra area. Finally, based on the ischemic penumbra area, tissue perfusion parameters, and brain tissue function parameters, a hierarchical decision model with a spatiotemporal attention mechanism is used to calculate the risk level and generate a corresponding clinical intervention plan. The present invention can achieve early and accurate screening and risk warning of acute stroke, and improve the efficiency of clinical intervention.

The invention discloses a biomedical multi-mode signal anomaly detection and prediction method and system, and the method comprises the steps: modeling a biomedical multi-mode signal into a time-varying non-local dynamic system, capturing the long-time-history dependence characteristic of the signal through a memory mechanism of a Caputo fractional derivative, and introducing a time-varying input item to process interference; the method comprises the following steps of: carrying out high-precision numerical integration by adopting an Adams-Bashform-Module solver; optimizing model parameters in combination with a local domain normalization pre-training strategy and a fusion loss function; abnormal detection is realized by calculating comparison between signal reconstruction deviation and a self-adaptive threshold value; a future anomaly probability is generated based on the trajectory prediction. According to the method, the problems of insufficient non-local dependence capture, poor time-varying interference robustness, high false positive rate and the like in the prior art are effectively solved, the accuracy and real-time performance of abnormal detection and prediction of biomedical signals such as electroencephalogram and electrocardiogram are remarkably improved, and the method is suitable for wearable medical equipment and clinical monitoring systems.

Provided herein are systems, kits, and methods for monitoring brain activity. In some implementations, a system includes a plurality of wearable sensors having a housing with an extended, rounded shape are removably attached to the scalp of a patient and monitor electroencephalogram (EEG) signals. Approaches for instructing a user to position and active that wearable sensors are disclosed. Approaches for facilitating collection, synchronization, and processing of EEG signals are disclosed. Approaches for handing off control of the wearable sensors between portable computing devices are disclosed.

The invention relates to the technical field of electroencephalogram analysis, in particular to a domain-adaptive cross-subject electroencephalogram signal emotion recognition method, which comprises the following steps: acquiring an electroencephalogram signal, and preprocessing the electroencephalogram signal; inputting the preprocessed electroencephalogram signals into a trained electroencephalogram signal emotion recognition model to obtain an emotion classification result; the electroencephalogram emotion recognition model comprises a graph convolution feature extraction module, an attention module, a domain confrontation module and a classifier module. According to the method, the confrontation module formed by combining the gradient inversion layer and the domain discriminator is designed, and the change rule of the electroencephalogram of a person in positive, neutral and negative states is focused instead of the intensity of the reaction, so that the extracted emotional features have domain invariance, and the extraction accuracy is improved. And identification deviation caused by difference of different individuals in cross-subject emotion identification is eliminated.

The invention relates to electroencephalogram signal acquisition, in particular to an electroencephalogram signal acquisition and monitoring system, which comprises a mobile terminal, a signal acquisition device, an EEG (electroencephalogram) signal, an fNIRS hemodynamic signal and IMU (inertial measurement unit) motion data, a physical information neural network method of a self-adaptive sampling strategy is adopted to identify a BCG vascular pulse pseudo-film segment in an EEG signal by using an fNIRS hemodynamic signal, and then a multiple linear regression model of motion artifacts is established based on the fNIRS hemodynamic signal and IMU motion data so as to identify a motion pseudo-film segment in the EEG signal. Performing interpolation restoration on the detected pseudo-film segments to eliminate artifacts in the EEG electroencephalogram signals, and drawing and displaying an electroencephalogram in real time according to the EEG electroencephalogram signals after the artifacts are eliminated; according to the technical scheme provided by the invention, the defect that BCG vascular pulsation artifacts and motion artifacts in the EEG signals are difficult to effectively eliminate can be effectively overcome.

The invention discloses an anesthesia depth monitoring system and method based on skin electric signal analysis, the system comprises a dynamic skin electric analysis subsystem, a dynamic signal enhancement and adaptive noise reduction processing subsystem, a polymorphic fusion depth network subsystem and a reinforcement learning driving adaptive regulator, and the method is realized based on the system. According to the invention, an accurate anesthesia depth monitoring scheme is provided through multi-modal analysis of skin electric signals; based on a self-adaptive drug adjustment algorithm and reinforcement learning, real-time adjustment of the anesthesia depth is realized, and too deep or too shallow anesthesia is prevented; individual anesthesia management is performed in combination with individual differences of patients, so that the anesthesia safety is improved; compared with a traditional electroencephalogram method, the GSR signal collection mode is noninvasive and more comfortable, and long-term monitoring of a patient is facilitated; through innovative signal processing, deep learning and adaptive adjustment mechanisms, a more intelligent, accurate and personalized anesthesia depth monitoring solution can be provided.

The present invention describes an artificial intelligence (AI) enabled electroencephalography (EEG) system that integrates Pathway Hierarchical Adaptive Referencing (PHAR) for localized signal detection, large language models (LLMs) for automated EEG reporting, and Internet of Medical Things (IoMT) connectivity for adaptive neuromodulation control. The system can also deliver transcranial electrical stimulation (tES) pulses and function as an electrical impedance tomography (EIT) system. PHAR employs a multi-layered multiplexer hierarchy and adaptive referencing topologies to optimize EEG signal acquisition and spatial resolution. LLM integration enables automated generation of human-readable EEG reports. IoMT connectivity allows closed-loop neuromodulation, where real-time EEG analysis guides the adjustment of stimulation parameters. The system can deliver tES pulses and perform EIT expands its functionality, allowing for targeted neuromodulation and impedance-based brain imaging. This integrated system revolutionizes EEG-based diagnostics, treatment, and research in neurology and neuroscience, offering a comprehensive and versatile tool for understanding and modulating brain function.