90 results about "Eeg signal analysis" patented technology

The invention belongs to the field of electroencephalogram signal analysis, and particularly relates to an asynchronous EEG-fNIRS-oriented brain image signal causal analysis method and device, and the method comprises the steps: creating a psychological experiment normal form for guiding a patient to carry out a cognitive ability test; collecting and preprocessing asynchronous EEG and fNIRS signals generated by the patient in the process of executing the psychological experiment normal form; extracting spatial features from the fNIRS signals to process EEG signals, so that the two signals are spatially aligned; time sequence features are extracted from the EEG signals to process the fNIRS signals, so that the time sequence features and the fNIRS signals are aligned; and analyzing the causal relationship between the EEG and the fNIRS signal after space and time alignment. According to the method, the spatial-temporal information of various modal signals collected by asynchronous non-joint equipment is fully utilized, the flexibility and accuracy of the method can support deep analysis of brain region states, and an effective tool is provided for neurovascular coupling analysis and clinical application of EEG and fNIRS.

The invention relates to a method for automatically identifying and removing ocular artifacts of multi-lead electroencephalogram signals based on a CycleGAN model, and belongs to the technical field of bio-electricity signal processing. The method comprises the steps that electroencephalogram signal data of a tested person is collected and preprocessed; extracting ocular artifacts, adding the ocular artifacts into an ocular artifact signal library, and adding the electroencephalogram signals without the ocular artifacts into a clean electroencephalogram signal library; a clean electroencephalogram signal and an ocular artifact are randomly selected, the ocular artifact is added into the clean electroencephalogram signal to obtain a noisy electroencephalogram signal, the clean electroencephalogram signal and the noisy electroencephalogram signal are input into a CycleGAN model, and the model is trained; the collected multi-lead electroencephalogram signal data are preprocessed and then input into the trained model, and eye artifacts of the multi-lead electroencephalogram signals are removed. Effective information in the electroencephalogram signals can be reserved for other electroencephalogram signal analysis or application, and eye artifacts in the multi-lead electroencephalogram signals can be recognized and removed.

The invention discloses an electroencephalogram fatigue recognition method based on multi-scale convolution and a double attention mechanism, and belongs to the technical field of electroencephalogram signal analysis and fatigue detection. The method comprises the following steps: carrying out preprocessing and data integration on multi-channel electroencephalogram signals, extracting sub-band energy by adopting wavelet packet decomposition, and mapping the sub-band energy to four frequency bands of delta, theta, alpha and beta; constructing a data matrix of channel * frequency band * time step, and inputting the data matrix into a multi-scale convolutional neural network to extract time domain features; a space attention mechanism is introduced, and key channel response is enhanced; time sequence characteristics are modeled through an LSTM network, a frequency attention mechanism is added, and a fatigue related frequency band is highlighted. According to the method provided by the invention, the modeling capability of time dependence and spatial frequency band correlation of the electroencephalogram signals is remarkably enhanced, and deep mining of fatigue state characteristics is realized. The model can realize efficient and accurate fatigue recognition without priori knowledge, and is suitable for an electroencephalogram intelligent monitoring scene in a complex environment.

The invention discloses a cognitive feature extraction and classification method and system based on electroencephalogram signals, and the method comprises the steps: constructing a cognitive divergence mapping network, so as to integrate a multi-branch brain region topology module and a neurodynamics physical information network module; the multi-branch brain region topology module divides detection branches of five brain regions (frontal lobe, central lobe, parietal lobe, occipital lobe and temporal lobe) according to 10-20 systems, and through feature extraction of different brain region branches, the feature learning ability of the Alzheimer's disease patient under the condition of cross-brain region signal heterogeneity is remarkably improved; the neurodynamics physical information network module can effectively extract changes of low-frequency and high-frequency components in electroencephalogram signals of the Alzheimer's disease patient by introducing frequency band separation, Fourier transform power spectrum constraint, an adaptive weighting mechanism and neural representation embedding in a frequency spectrum congruence space. The accuracy and generalization of electroencephalogram signal analysis are remarkably improved, and an efficient and non-invasive detection tool is provided for early diagnosis of the Alzheimer's disease.

The invention discloses a brain-computer interface implantation accuracy judgment method based on combination of images and electroencephalogram signals, and belongs to the field of biological cross scientific research. By combining an image processing technology and fine electroencephalogram signal analysis, the accuracy of electrode implantation is evaluated from two independent and complementary angles. Compared with a traditional method depending on postoperative histological analysis, the method has the advantages that multiple real-time feedback mechanisms are provided, and the accuracy and reliability of electrode positioning in an operation are remarkably improved. The technical innovation not only shortens the operation time and reduces the operation risk, but also improves the success rate of the whole operation process. Through real-time image and signal analysis, an operation team can adjust an operation strategy in real time, an electrode implantation path is optimized, and it is ensured that an electrode accurately reaches a target brain area. Besides, the delay and uncertainty of relying on postoperative slice analysis are avoided, so that the correctness of the electrode position can be confirmed during the operation, and the overall safety and effect of the brain-computer interface operation are enhanced.

The invention discloses a multi-level electroencephalogram signal analysis method, device and equipment based on space-time coding. The method comprises the steps that multiple pieces of electroencephalogram channel data to be analyzed are acquired; dividing the electroencephalogram channel data into a parallel group or a series group according to channel information corresponding to the electroencephalogram channel data; extracting multi-scale time domain and frequency domain features from each electroencephalogram channel data of the parallel group, and constructing a first feature set according to the multi-scale time domain and frequency domain features; extracting a time sequence dependence feature from each electroencephalogram channel data of the series group, and constructing a second feature set according to the time sequence dependence feature; determining a primary feature set and a secondary feature set according to the first feature set and the second feature set; dividing the main feature set and the secondary feature set into row and column coding branches, and obtaining electroencephalogram signal feature representation according to the row and column coding branches so as to complete cognitive analysis according to the electroencephalogram signal feature representation.

The invention is applicable to the technical field of deep learning, and provides a multi-concept identification network training method for electroencephalogram signal analysis, which comprises the following steps: constructing a multi-concept identification network for analyzing electroencephalogram signals, the multi-concept identification network comprises a first module constructed on the basis of an encoder-decoder and a second module which is constructed on the basis of a generative adversarial network and comprises a plurality of discriminators, semi-supervised learning training is carried out on the multi-concept identification network according to the electroencephalogram signal training set until a target loss function of the multi-concept identification network converges, and the multi-concept identification network is obtained. Therefore, dependence of the network on a large amount of marked data is reduced, generalization ability and robustness of the network in different patient groups are improved, heterogeneity and complexity of the electroencephalogram signals are coped with by paying attention to multiple discriminative perspectives, accuracy and reliability of electroencephalogram signal analysis are improved, and the method is suitable for popularization and application. The clinical application in diversified environments is facilitated.

A clustering integration-based electroencephalogram identification analysis method under complex visual stimulation comprises the following steps of designing an experimental normal form of electroencephalogram signal acquisition under a complex visual stimulation condition, selecting N subjects, and respectively operating according to the experimental normal form, synchronously collecting and recording multi-channel electroencephalogram signal data of N subjects under complex visual stimulation in the experimental normal form; starting an experiment, preprocessing electroencephalogram signals of the N subjects, obtaining preprocessed electroencephalogram signal data corresponding to the N subjects, and forming a rapid sequence to present an electroencephalogram signal data set; carrying out clustering analysis on the electroencephalogram signal data of the N subjects by utilizing a spectral clustering algorithm to obtain N sub-clusters; fusing the N tested sub-clusters by using a spectral clustering integrated optimization strategy based on hypergraph division; and testing and evaluating the fusion model to obtain a performance attribute value of the fusion model. According to the method, decision-making layer integration of a plurality of tested brain visual cognition image classification models is realized, so that a cross-individual electroencephalogram signal analysis method under a visual stimulation target with relatively high generalization, accuracy and robustness is constructed.

The invention discloses a classification method and device for multi-level feature fusion based on autism electroencephalogram signals, relates to the field of electroencephalogram signal analysis, and solves the problem that an existing deep learning method only pays attention to high-level features and neglects low-level features in autism electroencephalogram signal classification. The classification method comprises the following steps: acquiring an electroencephalogram signal of an autism patient, and executing a nonlinear multi-dimensional preprocessing step on the electroencephalogram signal; performing time domain-frequency domain double-domain joint feature extraction on the preprocessed electroencephalogram signals; constructing a hierarchical fusion and refinement module with decomposition-fusion-refinement three-stage processing capability; and based on the extracted hierarchical fused features, constructing a multi-level feature fusion model with an adaptive feature weight distribution mechanism, and training the multi-level feature fusion model based on autism electroencephalogram signals by adopting a self-supervised training strategy to realize classification of autism and non-autism samples. The method is also suitable for the field of deep learning electroencephalogram signal feature fusion.

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 invention provides a memory enhancement system and method based on a closed-loop regulation brain-computer interface, and relates to the technical field of medical equipment, and the system comprises a multi-channel neural activity detection unit which is used for collecting the field potential waveforms of a plurality of brain regions, related to memory cognition, of the brain of a target object; the electroencephalogram signal analysis unit is used for sending a corresponding control signal to the electrical stimulation output unit under the condition that the field potential waveform of any target brain region meets a preset triggering condition; and the electrical stimulation output unit is used for applying electrical stimulation to the target brain area under the condition of receiving the control signal sent by the electroencephalogram signal analysis unit. According to the memory enhancement system and method based on the closed-loop regulation and control brain-computer interface provided by the invention, by synchronously implanting the microelectrodes of the hippocampus multi-subregion and the temporal lobe cortex of the brain in a cross-scale manner, the waveform information of the memory coding spike ripple is effectively identified, and the accurate stimulation output moment is fed back; and a customized closed-loop brain-computer interface system memory regulation strategy can be provided for different individuals.

The invention discloses a virtual reality-based positive-feeling biofeedback system, which comprises a user evaluation module for acquiring psychological state information of a user through a psychological state evaluation questionnaire; the virtual reality equipment is used for providing a virtual reality scene for the user to carry out training; the guidance information is displayed in the virtual reality scene; the physiological signal detection device is used for collecting a physiological signal of the user in the process that the user executes the mind training by utilizing the virtual reality equipment; the physiological signals are synchronously displayed in the virtual reality scene; the electroencephalogram device is used for collecting electroencephalogram signals of the user in the process that the user executes the mind training through the virtual reality device; and the data analysis module is used for analyzing the psychological state cause of the user according to the psychological state information, the physiological signals and the electroencephalogram signals, generating an evaluation report according to an analysis result, and giving a mentality training suggestion according to the evaluation report, so that the intervention effect of mentality training on psychological diseases is improved.

The invention discloses an exoskeleton system based on man-machine symbiosis and a construction method, and aims to improve the suitability of a rehabilitation exoskeleton robot and people. Comprising a control system hardware platform, a teleoperation robot, a digital twinborn body of the teleoperation robot, an exoskeleton operator, a digital twinborn body of the exoskeleton operator, an exoskeleton robot and a digital twinborn body of the exoskeleton robot. By integrating various sensors, comprehensive acquisition of motion states and biological signals of exoskeleton operators is realized. In addition, the system further comprises an electroencephalogram signal analysis module and a voice semantic analysis module which are used for evaluating the concentration degree and intention of the user. According to the method, biomechanical consistency is ensured through a digital twinning technology, and autonomous learning and optimization adjustment are performed by using a large model, so that the functions of motion mode prediction, kinetic parameter optimization, personalized rehabilitation scheme suggestion and the like are realized. The method not only improves the effect and efficiency of rehabilitation training, but also enhances the user experience and the safety and reliability of the system.

The invention discloses a depression electroencephalogram signal classification method based on cross-layer confidence coefficient screening depth forest, which is characterized by comprising the following steps of: S1, acquiring depression electroencephalogram signal data of Jidai courtyard, carrying out time domain and frequency domain feature conversion, carrying out preprocessing operation on the data, and dividing the data into a training set and a test set; and S2, constructing a depression electroencephalogram signal classification model with a cross-layer confidence screening function based on a deep forest technology. The model is composed of two main modules of multi-granularity scanning and cascade forests, and each cascade forest comprises two random forests and two complete random forests; s3, in the training process, training data are sent into the cross-layer confidence screening electroencephalogram signal classification model, the model carries out detailed sliding window analysis on the data by utilizing multi-granularity scanning, and time domain and frequency domain features of the processed electroencephalogram signals obtained during data preprocessing are spliced to obtain enhanced features; s4, the enhanced features screen a cascade forest layer by layer through cross-layer confidence, output of each layer is a prediction result, operation of crossing cascade layers is achieved through confidence screening and comparison, and finally a trained depression electroencephalogram signal classification model is formed. Compared with the prior art, time domain and frequency domain feature splicing is added in the multi-granularity scanning stage, a cross-layer confidence coefficient screening mechanism is added in the cascade forest stage, and the feature extraction capacity of the model for complex electroencephalogram data is enhanced, so that key information is captured and analyzed more accurately, the performance of the model is effectively optimized, and the method is suitable for being popularized and applied. The classification accuracy and efficiency are improved, and powerful support is provided for the development of the real-time electroencephalogram signal analysis and application field.

The invention discloses a driving fatigue relieving system based on a brain-computer interface, and the system comprises an electroencephalogram signal collection module which is used for collecting the electroencephalogram data of a driver in real time; the electroencephalogram signal analysis module is connected to the electroencephalogram signal acquisition module and is used for processing and analyzing the electroencephalogram data received from the electroencephalogram signal acquisition module so as to judge the mental state of the driver; and the execution application module is connected to the electroencephalogram signal analysis module and is used for executing a corresponding auxiliary strategy based on an analysis result of the electroencephalogram signal analysis module. According to the driving fatigue relieving system based on the brain-computer interface, a direct interaction control channel between the brain of the driver and the external equipment is constructed, so that the mental state of the driver is monitored and intervened in real time, and a new technical means is provided for improving driving safety and reducing traffic accident risks.

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 present invention provides an electroencephalogram (EEG) signal analysis method, system and medium for cognitive function detection, which relate to the technical field of electroencephalogram (EEG) signal analysis, and include the following steps: preprocessing collected EEG signals; extracting signal features from the preprocessed EEG signals; analyzing the feature correlation of time domain features and frequency domain features based on signal features of normal volunteers and volunteers with cognitive impairment; selecting signal features with the largest feature correlation as evaluation features, and analyzing the evaluation features; judging whether a user has cognitive dysfunction; the present invention is used to solve the problem that the existing electroencephalogram (EEG) signal analysis technology, when used for cognitive function analysis, still does not consider whether different features have the greatest impact on cognitive function when considering influencing factors, resulting in insufficient accuracy in evaluating cognitive function.

The invention discloses an emotion calculation-oriented electroencephalogram feature visual analysis method and system, and aims to solve the problems of individual difference and low signal-to-noise ratio in electroencephalogram signal analysis, and the method comprises the following steps: (1) feature optimization: designing a progressive optimization algorithm to eliminate individual difference of electroencephalogram emotion features; (2) feature evaluation: constructing a multi-dimensional evaluation system, and verifying the effectiveness of a feature optimization algorithm; (3) abnormal feature inspection: positioning and processing abnormal features; and (4) feature export: realizing export of an optimized feature set and generation of a visual report. According to the method, engineering application of the visual analysis method is achieved by constructing a visual system, and a systematic solution is provided for the electroencephalogram characteristic quality problem in sentiment calculation research.

The application relates to the technical field of human-computer interaction, in particular to a mechanical arm path planning method based on an emotional brain-computer interface, which comprises the following steps: S1, a user watches a video collected by a camera, visual tracks of the user are recorded through an eye tracker, and electroencephalogram data of the user are synchronously collected; S2, emotions of the user are analyzed according to the electroencephalogram signals, and the electroencephalogram signals are classified into positive emotions, neutral emotions and negative emotions; S3, eye movement data are analyzed, and three-dimensional space coordinates of a user's gaze are located; S4, positive and negative emotions of the user are valued in a three-dimensional space environment according to the three-dimensional space coordinates of the user's gaze, and a total reward value graph is obtained; and S5, a path is planned according to a greedy algorithm and the total reward value graph, until a target position is reached, and path planning is completed. The application improves the efficiency of mechanical arm path planning, and the planned path is more in line with human expectations.

The present invention proposes a multi-source domain EEG signal analysis method with multi-modal representation. The present invention first uses multi-manifold mapping to extract the common invariant representation of the multi-source domain and the target domain, while taking into account the low-dimensional structure and multivariate statistical characteristics of the EEG signal. At this stage, the CORAL loss is calculated to guide the model to obtain high-quality common invariant representation. Secondly, the multi-source domain is decomposed and one-to-one feature extraction is performed. At this time, the MMD loss is used to guide the model to obtain high-quality private invariant representation. Finally, a softmax classifier is used for classification. The effectiveness of the method was evaluated on the public MI1 and MI2 datasets, as well as the EEG signal dataset collected by the team. Experimental results show that the present invention performs superiorly in multi-subject scenarios.

The invention discloses an intelligent disturbance of consciousness assessment system based on electroencephalogram, and relates to the technical field of disturbance of consciousness assessment. Comprising an audio acquisition module, a voice feature extraction module, a voice simulation module, a voice generation module, a speech generation module, a picture acquisition module, a picture generation module, a video generation module, a background setting module, a character model generation module and a character feature extraction module. According to the intelligent assessment system for disturbance of consciousness based on electroencephalogram, pictures and videos are played through the display to stimulate a patient, audios are played through the earphone to stimulate the patient and the tactile sense of the patient, and electroencephalogram signals of the patient in the testing process are collected; the electroencephalogram signals of the patient are analyzed through electroencephalogram signal analysis software, the disturbance of consciousness of the patient is recognized and evaluated according to the electroencephalogram signal analysis result of the patient, and therefore compared with an experimental result obtained through a traditional testing mode, the accuracy and objectivity are higher.

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.