41 results about "Eeg signal analysis" patented technology

The invention discloses an intelligent user assisting method and device integrating visual sensing and electroencephalogram sensing. The user assisting method comprises the steps that an environment image of an environment where a user is located is recognized through an image content understanding model, corresponding text information is generated, and the text information is used for indicating environment information of the environment where the user is located; the collected electroencephalogram signals of the user are analyzed through an electroencephalogram signal analysis model, state information of the user in the current environment is determined, and the state information can indicate whether the user is in a cognitive state, a recall state or a resting state at present; and user demands are predicted through a large language model according to the text information and the state information, corresponding dialogue information with the user is generated, and the dialogue information is used for assisting the user.

The invention discloses an electroencephalogram fatigue detection method based on fusion of a graph convolutional network and Transform, and belongs to the technical field of artificial intelligence and electroencephalogram signal analysis. The method comprises the steps that multichannel electroencephalogram signals are collected, and band-pass filtering, power frequency notch, independent component analysis, standardization and other preprocessing are conducted on the signals; constructing an inter-channel graph structure based on a Pearson's correlation coefficient, and extracting spatial features by using a graph convolutional network; inputting the spatial features of the plurality of continuous time windows into Transform to carry out time sequence modeling; and finally, realizing fatigue state recognition through a full-connection network and a Softmax classifier. According to the method, the spatial topological structure and the time dynamic evolution of the EEG signal can be modeled at the same time, the accuracy and the real-time performance of fatigue detection are remarkably improved, and the method has good generalization ability and edge deployment ability and is suitable for traffic driving monitoring, intelligent health and other scenes.

The application discloses a brain network analysis method based on a hypergraph and a gravity model, relates to the technical field of electroencephalogram signal analysis and complex network science, and comprises the following steps: S1, acquiring multi-channel stereoelectroencephalogram signals, and calculating the phase locking values between each pair of channels; and S2, based on the phase locking values, taking the stereoelectroencephalogram channels as nodes, and constructing a 3-consistent weighted hypergraph, wherein each hyperedge in the 3-consistent weighted hypergraph comprises three nodes, and the nodes in the 3-consistent weighted hypergraph are divided into multiple groups; the driving force between groups is obtained through layer-by-layer deduction, the high-order correlation characteristics between brain groups can be reflected in multiple dimensions, the interaction of different brain groups can be quantified from the aspect of the driving direction, the multiple quantitative indexes derived can enrich the analysis dimension of the driving relationship between brain groups, and the method is suitable for various brain signal research scenes, so as to meet the actual research and use requirements of the fine analysis of brain interaction mechanisms.

The invention provides a lower limb rehabilitation evaluation method, system and device based on a brain-computer interface and a medium, and relates to the technical field of rehabilitation medicine and medical electronics. According to a lower limb rehabilitation evaluation technology based on a brain-computer interface, a two-way evaluation system of peripheral muscle form-central nervous activity is constructed through synchronous acquisition of wearable A-type ultrasound and electroencephalogram signals; a-type ultrasound monitors the change of the thickness of the rectus femoris in real time, and electroencephalogram signals analyze the coherence of Alpha / Beta frequency bands, so that functional difference evaluation of a stroke patient in a resting state, a passive motion state and an active motion state with different resistances is realized, the limitation of single subjective scoring of a traditional scale is avoided, and a multi-dimensional quantitative index is provided for rehabilitation evaluation.

The invention belongs to the technical field of electroencephalogram signal analysis, and particularly relates to an electroencephalogram signal-based fatigue state detection system, which comprises a signal acquisition module, a signal preprocessing module, a feature extraction module and a fatigue state judgment model, the signal acquisition module is used for establishing stable low-impedance connection between each electrode and scalp by wearing an electrode cap and using conductive paste, and recording an original EEG signal; the signal preprocessing module is used for carrying out band-pass filtering and self-adaptive wave trapping on an original EEG signal and then removing physiological artifacts through self-adaptive filtering and independent component analysis; the feature extraction module is used for extracting artificially designed frequency domain, time domain and nonlinear features from the preprocessed signals, automatically learning deep features through 1D-CNN and an attention mechanism, and splicing the two types of features to form a fusion feature vector; the fatigue state judgment model calculates a fatigue score based on the fusion feature vector, and obtains a comprehensive score through time smoothing and trend analysis.

The method for analyzing incomplete channel EEG signals based on adaptive orthogonal regression belongs to the field of EEG signal analysis and pattern recognition technology. This invention proposes an embedded feature selection framework for incomplete channel EEG data, enabling robust modeling directly without channel completion and effectively reducing the impact of missing channels on the analysis results. By introducing a channel missingness perception mechanism and an adaptive channel weighting strategy, this invention achieves dynamic modeling of the contribution of information from different channels, significantly improving the model's stability in multi-channel and high-missing-rate scenarios. The orthogonal regression-based feature selection mechanism can maintain the integrity of discriminative information while suppressing redundant features, making it suitable for high-dimensional EEG data analysis tasks. This method has good versatility and can be adapted to different channel scales (such as few channels and high-density channels) and single-modal or multi-modal fusion scenarios, possessing high engineering application value.

The application provides a brain-computer interface cognitive function decline monitoring system, method, device and medium, which can be applied to the field of electroencephalogram signal analysis. The brain-computer interface cognitive function decline monitoring system comprises a signal monitoring module, a signal processing module, a result processing module and a medium. The signal monitoring module is used for collecting multi-channel electroencephalogram signals in multiple time periods. The signal processing module is used for performing time-frequency decomposition and global field power determination on the multi-channel electroencephalogram signals. The probability of the multi-channel electroencephalogram signals belonging to each microstate mode is determined, and a microstate feature vector is generated. According to the phase locking value of the electroencephalogram signals between nodes and the similarity between the microstate feature vectors corresponding to the nodes, a topological heterogeneous brain network is generated. The topological heterogeneous brain network is subjected to graph feature extraction and state classification to obtain an analysis result. The result processing module is used for generating a brain load change trend according to multiple analysis results, and generating an adjustment suggestion based on the brain load change trend.

The invention discloses a training method, an analysis method, equipment and a program product of an electroencephalogram analysis model. In the training method, a first network is adopted to extract graph features of electroencephalogram signals to be analyzed as semantic features; using a second network to extract graph features of the to-be-analyzed electroencephalogram signals as domain change features; mapping the semantic features and the domain change features into a Granger causal relationship matrix by adopting a causal representation network; adopting a target task network to execute a target task according to the Granger causality matrix; performing individual classification according to the domain change characteristics by adopting a domain classification network; and updating parameters of the first network, the second network, the causal representation network, the target task network and the domain classification network according to target task loss and individual classification loss. The training method has good field generalization ability.

The application discloses a brain-computer interface system and method, which comprises a visual stimulation module, a collection module and an analysis module. The visual stimulation module provides a plurality of visual stimulation targets, each of which comprises a background gray adjustable display area and a visual stimulation area formed in the display area. The visual stimulation area does not completely fill the display area, and the display area and the visual stimulation area jointly constitute a visual stimulation code. The collection module is used for collecting brain electrical signals generated by a user to the visual stimulation code. The analysis module extracts features in the brain electrical signals and identifies a visual stimulation target currently gazed by the user. The application adopts a contrast coding method and can be used on a common refresh rate display to realize a high-frequency multi-target steady-state visual evoked potential brain-computer interface system.

The invention relates to a hearing test data validity evaluation method and system adopting artificial intelligence, and relates to the technical field of big data analysis and evaluation, and the method comprises the steps: collecting hearing test data of a target object, and synchronously collecting a resting electroencephalogram signal of a preset resting period before each stimulus is given; acquiring historical hearing test data of a target object, and analyzing and acquiring a test evaluation input set in combination with the hearing test data and the resting electroencephalogram signal; and according to the test evaluation input set, activating a preset data evaluation channel to perform data validity evaluation, and obtaining a validity evaluation result. The problems that traditional hearing test data validity evaluation mostly depends on single test data, physiological state information of a test object and a historical test background are not fully associated, and the evaluation method is relatively simple and is easily influenced by environment or object state fluctuation, so that the evaluation accuracy is insufficient are solved.

A time-frequency aligned EEG signal analysis method, apparatus, device, and medium are disclosed, aiming to consider and reduce the temporal and frequency distribution differences of EEG signals, thereby more effectively improving the model's generalization ability across different subjects or experimental sessions. The method involves time-frequency decomposition of the EEG signal, calculating global statistics in each frequency band, and performing whitening transformation to calibrate the signal. Finally, the multi-channel EEG signal is reconstructed to obtain a time- and frequency-aligned multi-channel EEG signal. This invention's time-frequency alignment process comprehensively reduces the distribution differences between EEG data from different sources in both time and frequency dimensions. Compared to methods that only align the time domain, it more effectively improves the model's performance on new user or new session data, thus more effectively enhancing the model's generalization ability across different subjects or experimental sessions.

The invention discloses an automatic sleep staging method based on EEG (electroencephalogram) signals, relates to the field of intelligent health monitoring and electroencephalogram signal analysis, and aims to solve the problems of insufficient feature extraction and low staging precision of an existing method. The method comprises the following steps: constructing a space-time diagram construction unit (STOT), introducing an optimal transmission algorithm to adaptively model brain region association, and generating an interpretable space-time dynamic map; and an improved double-branch U-shaped network (USleepNet) is designed, and multi-scale features and an attention mechanism are fused to improve the classification performance. Experimental results show that the method has relatively high accuracy and robustness on an ISRUC-S1 / S3 data set, and has a good application prospect.

The invention provides an emotion monitoring system and method based on a multi-modal signal, and the method comprises the steps: monitoring and obtaining a PPG signal in real time, and inputting the PPG signal to a PPG signal analysis model to obtain a first emotion analysis result; and performing emotion abnormity judgment based on the result, and if the emotion is abnormal, starting acquisition and analysis of the electroencephalogram signal and the audio and video signal. Wherein the electroencephalogram signals are processed by an electroencephalogram signal analysis model which is pre-trained by the sleep state data and migrated to the resting state data to be trained, and a second emotion analysis result is obtained; the audio and video signals are processed by the corresponding analysis model to obtain a third emotion analysis result. And finally, performing multi-modal joint decision based on the three results, and outputting a target emotion analysis result. According to the emotion monitoring system and method based on the multi-modal signals, multi-modal signal fusion is achieved based on a two-stage monitoring mechanism and a mobility learning strategy, the limitation of a single signal source is made up, and the robustness and accuracy of emotion state classification are improved.

The present application relates to the technical field of biomedical signal processing and artificial intelligence, and discloses a training method, an analysis method and equipment of an electroencephalogram signal analysis model, which comprises the following steps: obtaining an electroencephalogram signal sample set for training, wherein a relative comparison label is a label used to represent the relative comparison result between electroencephalogram signal pairs, and an absolute classification label is a label used to represent the comparison between the electroencephalogram signal sample and a preset comparison standard; extracting the signal features of the electroencephalogram signal sample set through a feature extraction module; calculating an output classification prediction result based on the signal features through a classification prediction module; calculating an absolute classification loss based on the classification prediction result and the absolute classification label, and calculating a relative ranking loss between the electroencephalogram signal pairs based on the classification prediction result and the relative comparison label; obtaining a loss function based on the preset constraint condition and the weighted absolute classification loss and relative ranking loss; updating the parameters of the classification prediction module and the feature extraction module based on the loss function to obtain an electroencephalogram signal analysis model.

The invention relates to the technical field of brain-computer interfaces, in particular to a brain-controlled enteroscope operating system based on mixed reality. According to the system, an endoscope real-time image is obtained through a head-mounted display visual induction module, and a visual stimulation signal is generated on a display interface based on a preset visual induction stimulation normal form. An electroencephalogram signal generated by the target object for the visual stimulation signal is acquired through an electroencephalogram signal acquisition module, and operation intention information of the target object is determined based on the electroencephalogram signal through an electroencephalogram signal analysis module. The main control module is used for generating the corresponding enteroscope control instruction based on the operation intention information, and the enteroscope auxiliary manipulator module is used for executing the corresponding enteroscope control operation based on the enteroscope control instruction, so that the accuracy and the safety of the enteroscope operation are improved.

The invention of intelligent system for detection and classification of individual capabilities through brain mapping and advanced EEG signal analysis using deep learning relates to a method capable of identifying and mapping an individual's cognitive strengths and weaknesses based on the impact of each brain region on the individual's performance In this invention, a combination of data and information from cognitive assessment databases, along with rules extracted from previous research and studies, and results obtained from individuals' brain signals in electroencephalography are aggregated to create an enhanced collective trained model for generating the individual's brain map and identifying the individual's cognitive strengths and weaknesses based on the obtained results. Rapid and accurate data processing, coupled with the use of modern deep learning and statistical techniques, has transformed this system into a powerful tool for better understanding brain function and its clinical and research applications.

The embodiment of the invention provides an emotion recognition method based on electroencephalogram signal enhancement. The method is applied to the field of electroencephalogram signal analysis, and comprises the following steps: performing time-frequency significance analysis on electroencephalogram signals to determine time-frequency significance weights of the electroencephalogram signals at each time point; performing time sequence consistency clustering on the electroencephalogram signals to obtain a fragment division result of the electroencephalogram signals; processing the electroencephalogram fragments by adopting a soft attention mechanism to obtain segmentation features of the electroencephalogram; performing parallel convolution on the segmented features of the electroencephalogram signals to obtain multi-scale features; performing feature splicing and gating feature fusion on the multi-scale features to obtain enhanced features; carrying out linear projection on the enhanced features to obtain a de-noised electroencephalogram signal; extracting multi-source emotional features from the de-noised electroencephalogram signals, and splicing the extracted multi-source emotional features to form an emotional feature vector; and the emotion feature vectors are analyzed and processed to obtain an emotion recognition result, so that the accuracy and reliability of emotion recognition are improved.

The invention provides an electroencephalogram anomaly positioning method and system based on polymorphic biological signs and motion information, and the method comprises the steps: processing an electroencephalogram signal through electroencephalogram signal preprocessing, feature extraction, modeling classification and the like; the electroencephalogram signals are transmitted to an abnormality judgment module, and whether the electroencephalogram signals of the testee are in an electroencephalogram abnormal signal type or not is judged; the captured position information of the six points is transmitted to an abnormity judgment module, and whether the motion information of the testee is abnormal or not is judged; the method comprises the following steps: collecting heart rate, pulse, blood pressure, electrodermal response, body temperature and other biological sign data of a testee; according to a preset normal value of the biological signs, judging whether the testee has abnormal biological signs or not; and recording brain wave data of the testee, and marking and positioning the brain wave signals meeting the standard time period. According to the method, a traditional electroencephalogram signal analysis method depending on a live-action video is abandoned, the workload of medical staff is effectively relieved, and meanwhile the method is expanded to the field of biological data collection to obtain user emotion fluctuation information.

Systems and methods for EEG signal analysis configured to identify random and harmonic segments of an EEG signal associated with brain activity of a subject, and determine and display at least one or both of: a filtered EEG signal from the EEG signal, wherein the identified random and harmonic segments of the EEG signal have been which have been removed and the remaining portion of the EEG signal is displayed, and / or a non-filtered or partially filtered EEG signal from the EEG signal wherein the identified random and harmonic segments are indicated or identified in the display.

The invention discloses an electroencephalogram signal super-resolution generation method and device, equipment and a storage medium, and relates to the technical field of medical artificial intelligence. The low-resolution ECoG signals are preprocessed; presetting a target super-resolution ECoG signal channel layout, and identifying a virtual channel in the target super-resolution ECoG signal channel layout based on the channel layout of the low-resolution ECoG signal; filling Gaussian noise on a time sequence corresponding to a virtual channel in the target super-resolution ECoG signal channel layout to obtain a noise signal; taking the preprocessed low-resolution ECoG signal as a real signal and mixing the real signal with a noise signal to obtain a mixed signal; and performing iterative reverse diffusion denoising processing on the mixed signal based on an unconditional DDPM model and a Repaint strategy to obtain a super-resolution ECoG signal. According to the method, the invasiveness, the operation risk and the cost of ECoG records can be reduced, and meanwhile, the spatial resolution, the accuracy and the reliability of electroencephalogram signal analysis are improved.

The application is suitable for the technical field of computers, and provides an electroencephalogram analysis device, method, electronic device and storage medium. The electroencephalogram analysis device comprises a collection module, a processing module and an analysis module. The collection module is used for collecting an electroencephalogram of a target to be analyzed and transmitting the electroencephalogram to the processing module. The processing module is used for receiving the electroencephalogram and pre-processing the electroencephalogram to obtain a plurality of electroencephalogram input data, and then transmitting the plurality of electroencephalogram input data to the analysis module. The analysis module is internally provided with a trained electroencephalogram analysis model. The analysis module is used for receiving the plurality of electroencephalogram input data, inputting the plurality of electroencephalogram input data into the electroencephalogram analysis model, and obtaining an analysis result output by the electroencephalogram analysis model. The analysis result represents the possibility of the target to be analyzed suffering from Parkinson's disease. The application can timely and accurately find out whether the target to be analyzed has the possibility of suffering from Parkinson's disease.

The application discloses a kind of adaptive adjustment cap and system for electroencephalogram acquisition, it is related to medical instrument technical field, including synchronous acquisition module, rhythm identification module, drift analysis module, adjustment trigger module and phase adjustment module: synchronous acquisition module, in electroencephalogram acquisition process, each partition air pressure variation process and electrode position change process are synchronously collected, and corresponding time sequence change record is formed according to uniform time scale, and the air pressure variation rate result of each partition is calculated and formed, and the air pressure variation rate result of each partition is recorded in the tail of time sequence change record.The application synchronously collects air pressure variation and electrode position data by uniform time scale, identifies partition rhythm difference and is associated with spatial deviation, generates drift identification in advance, realizes abnormal positioning;Meanwhile, by phase reverse progression and alternate staggered peak adjustment, reconstruct air pressure variation rhythm, restore overall contraction consistency, stabilize electrode spatial position, guarantee brain area positioning accuracy and electroencephalogram signal analysis reliability.

The invention discloses an electroencephalogram signal analysis network training method and application based on chaos edge dynamics state regulation and control, and belongs to the field of electroencephalogram signal analys.The method comprises the steps that the dynamics state of an electroencephalogram signal analysis network serves as the direction of electroencephalogram signal analysis network training, hidden layer data of the electroencephalogram signal analysis network is sampled in real time, and the electroencephalogram signal analysis network is obtained; calculating a dynamic state parameter of the electroencephalogram signal analysis network under a short cycle time step length based on the hidden layer data, wherein the dynamic state parameter is used for quantifying a cycle layer dynamic state of the electroencephalogram signal analysis network; then, the gradient of the electroencephalogram signal analysis network weight is set based on the dynamic state parameters, the dynamic state is converged to the chaos edge, training of the electroencephalogram signal analysis network can be completed in a short cycle time step, the problems of gradient disappearance and explosion of traditional BPTT are solved, and the prediction precision, efficiency and reliability of electroencephalogram signals are improved.

The invention relates to the technical field of medical detection, and discloses an electroencephalogram signal-based neuropathic pain detection and analysis system, which comprises an electroencephalogram signal acquisition module, a signal preprocessing module, a feature extraction module, a pain detection and analysis module, a result output module and a system calibration module which are electrically connected in sequence to form a closed-loop detection and analysis system, objective detection of neuropathic pain is achieved, subjective description of a patient does not need to be depended on, misdiagnosis and missed diagnosis caused by subjective factors are effectively avoided through pure objective electroencephalogram signal analysis, and a reliable objective basis is provided for clinical diagnosis and treatment; the electroencephalogram signal acquisition precision is high, electroencephalogram signals of different scalp areas are comprehensively acquired through the 32-channel electrode array, various interference signals are effectively filtered out in combination with an improved self-adaptive filtering algorithm, the signal quality is ensured, and a reliable basis is provided for feature extraction and detection analysis.

This application provides a method, system, and electronic device for assessing motor intention, relating to the field of electroencephalogram (EEG) signal analysis and processing technology. The method includes acquiring raw EEG signals from 62 channels of a user; selecting channels from the raw EEG signals to determine a first EEG signal; preprocessing and feature construction of the first EEG signal to obtain an adjacency matrix of the EEG signal; and determining the causal relationship between the associated edges of the channels using a channel determination model based on the adjacency matrix of the EEG signal, thereby assisting in assessing the user's motor intention.

This invention discloses a method, apparatus, electronic device, and storage medium for analyzing electroencephalogram (EEG) signals. The method includes: acquiring an EEG signal to be analyzed and preprocessing the EEG signal; extracting a target EEG signal from the preprocessed EEG signal and generating an EEG signal topographic map sequence based on the target EEG signal; and inputting the EEG signal topographic map sequence into a preset EEG signal analysis model to obtain a classification result of the EEG signal to be analyzed. The preset EEG signal analysis model includes a convolutional neural network module, a long short-term memory network module, and a fully connected network module. The technical solution of this invention solves the problem in existing EEG signal analysis algorithms where data features are not fully mined, leading to low recognition accuracy of target EEG signals. It achieves deep learning of EEG signal features, improving the accuracy of EEG signal analysis results.

The invention discloses a time-frequency aligned electroencephalogram signal analysis method, device and equipment and a medium, and aims to consider and reduce the distribution difference of electroencephalogram signals in time and frequency so as to more effectively improve the generalization ability of a model across different subjects or different experimental sessions. Global statistics are calculated on each frequency band, whitening transformation is carried out to calibrate the signals, and finally the multichannel electroencephalogram signals are reconstructed to obtain the multichannel electroencephalogram signals aligned in time and frequency. Through the time-frequency alignment processing provided by the invention, the distribution difference between electroencephalogram data of different sources can be more comprehensively reduced from the two dimensions of time and frequency, and compared with a method of only time domain alignment, the expression of the model on new user or new session data can be more effectively improved, and the user experience is improved. Therefore, the generalization ability of the model across different subjects or different experimental sessions is improved more effectively.

The application discloses a single-channel electroencephalogram driving fatigue recognition method based on a time-frequency attention network. It belongs to the field of electroencephalogram signal analysis, and the operation steps are: driving experiment paradigm design and single-channel electroencephalogram signal acquisition; the collected electroencephalogram signals are pretreated; the pretreated electroencephalogram data are subjected to continuous wavelet transformation to obtain the spectrum-time representation corresponding to each data; the spectrum-time representation of each sample is input into a time-frequency attention network model, so that the model automatically extracts valuable feature information and completes the recognition of the fatigue state. The spectrum-time representation of the single-channel electroencephalogram signal is combined, a time-frequency attention mechanism and an adaptive feature fusion module are used to fully mine and capture key features related to driving fatigue, and the recognition of the driving fatigue state is realized; the method is reasonable in design, convenient to realize, good in detection effect and high in practical value.

The invention relates to the technical field of biomedical engineering, and provides a biofeedback-based senile depression acousto-optic hypnosis system, which is characterized in that electroencephalogram signal curves of a resting state and a stimulation state of a plurality of electrodes are acquired for a patient through an electrode cap; the power spectrum density of each electrode is obtained, and then the senile attenuation index is obtained; obtaining a reduction amplitude according to the senile attenuation index, and obtaining a reduction electroencephalogram signal curve of each electrode; segmenting to obtain a plurality of local electroencephalogram curves; obtaining the entrainment generation probability of each local electroencephalogram curve; screening to obtain a plurality of entrained electroencephalogram curves of the reduced electroencephalogram signal curve of each electrode; and the final electroencephalogram signal curve of each electrode is comprehensively obtained, so that the asymmetry index is recalculated, and acousto-optic hypnosis analysis and evaluation are assisted. The invention aims to solve the problem that the brain entrainment effect of the elderly patient fails to affect the analysis and judgment of the acousto-optic hypnosis electroencephalogram signal.

The invention relates to the technical field of electroencephalogram signal processing, in particular to a personalized music generation method and system based on electroencephalogram signal analysis, and the method comprises the steps: collecting multivariate data, and carrying out the correlation fusion of the multivariate data, and obtaining correlation fusion data; constructing a user psychological feature data set through multi-dimensional data structuring; extracting current psychological state core features and long-term historical trend features of the user, and establishing a dual-feature and music parameter collaborative mapping model; fusing the current psychological state core features and the long-term historical trend features of the user through a double-feature weighted fusion algorithm, and optimizing to generate initial music; setting a treatment cycle, respectively collecting psychological state scores of the user before and after the treatment cycle, and judging a treatment effect grade in combination with a preset standard; and generating optimized personalized music in combination with the double-feature and music parameter collaborative mapping model and the long-term historical trend features. According to the scheme, accurate generation of personalized music fitting the dynamic psychological state of the user can be realized.