64 results about "Eeg signal analysis" patented technology

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 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 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 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 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 present invention relates to the technical field of neural decoding, and particularly relates to a lightweight motor imagery classification method and device guided by frequency prior features. The method includes: extracting the time-domain features of high-dimensional EEG signals; performing weighted integration on the EEG signals collected by different electrodes through a spatial convolutional layer; performing downsampling through average pooling to compress the high-frequency information of the weighted integrated EEG signals; inputting the compressed EEG signals into a residual network to process the high-frequency information of the compressed EEG signals and further optimize the extraction of signal features; designing causal convolution and dilated convolution to effectively capture the temporal features of EEG signals and ensure that the network structure has good computational efficiency and interpretability. This method provides an efficient and interpretable solution in EEG signal analysis and can significantly improve the performance of the model.

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.

A classification device based on multi-level feature fusion of autistic EEG signals relates to the field of EEG signal analysis and solves the problem that existing deep learning methods only focus on high-level features while ignoring low-level features in the classification of autistic EEG signals. The classification method includes the following steps: obtaining EEG signals of autistic patients and performing nonlinear multidimensional preprocessing steps on the EEG signals; performing time-domain-frequency-domain dual-domain joint feature extraction on the preprocessed EEG signals; constructing a hierarchical fusion and refinement module with a three-stage processing capability of decomposition-fusion-refinement; based on the extracted hierarchical fusion features, constructing a multi-level feature fusion model with an adaptive feature weight allocation mechanism, and using a self-supervised training strategy to train the multi-level feature fusion model based on autistic EEG signals to achieve the classification of autistic and non-autistic samples. The present invention is also applicable to the field of deep learning EEG signal feature fusion.

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 signal analysis method and system, and relates to the technical field of signal processing.The method comprises the steps that electroencephalogram signals are obtained, the data form of the electroencephalogram signals is a two-dimensional matrix, each row of the two-dimensional matrix corresponds to sampling data of one time point, and each column of the two-dimensional matrix corresponds to one sampling channel; performing data preprocessing operation on the electroencephalogram signal, and inputting a preset electroencephalogram signal analysis model to obtain an electroencephalogram signal analysis result output by the electroencephalogram signal analysis model; the electroencephalogram signal analysis model extracts local space features in the preprocessed electroencephalogram signals through a lightweight convolutional neural network module of the electroencephalogram signal analysis model and outputs the local space features to a Transformer module of the model, and the Transformer module captures global time features based on the local space features and outputs the global time features to an output module of the model; and the output module generates and outputs an electroencephalogram signal analysis result according to the global time characteristics. According to the method, the electroencephalogram signal analysis efficiency can be improved while the accuracy of the electroencephalogram signal analysis result is ensured.

The present invention belongs to the field of electroencephalogram (EEG) signal analysis, and specifically relates to a method and device for causal analysis of brain imaging signals for asynchronous EEG-fNIRS, including: creating a psychological experiment paradigm for guiding patients to perform cognitive ability tests; collecting and preprocessing asynchronous EEG and fNIRS signals generated by patients during the execution of the psychological experiment paradigm; extracting spatial features from the fNIRS signals to process the EEG signals to align them spatially; extracting temporal features from the EEG signals to process the fNIRS signals to align them temporally; and analyzing the causal relationship between the EEG and fNIRS signals after spatial and temporal alignment. The present invention realizes the full utilization of spatio-temporal information of multiple modality signals collected by asynchronous non-union devices, and its flexibility and accuracy can support in-depth analysis of brain region states, providing an effective tool for neurovascular coupling analysis and clinical application of EEG and fNIRS.

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.