57 results about "Eeg classification" patented technology

The invention discloses an emotion recognition system based on depth learning and a brain-computer interface as well as application. The emotion recognition system comprises portable EEG acquisition equipment, an emotion EEG classification module and an emotion classification display module, wherein the portable EEG acquisition equipment acquires emotion EEG signals from the brain of a subject; the emotion EEG classification module analyzes the emotion EEG signals, establishes a relation model between the EEG signals and emotions, and performs emotion classification on the input emotion EEG signals through the relation model; and the emotion classification module displays recognized emotion types and prompts the emotion state of the subject. The emotion recognition system based on depth learning and the brain-computer interface as well as application can realize accurate acquisition, effective identification and accurate classification of the emotion EEG signals, directly promote the emotion state and realize an emotion state monitoring function. The corresponding relation between the EEG signals and the emotion types is directly established, and feedback of emotions is realized.

An electroencephalogram (EEG) classification detection device based on lacuna characteristics belongs to the technical field of electroencephalogram automatic detection. The EEG classification detection device comprises a multi-way EEG amplifier, a data collection card and a computer which are sequentially connected through a circuit. A signal preprocessing module, a signal segmentation module, a lacuna characteristic extraction module, a Bayes linear discriminant analysis classification module and a threshold judgment module are built in the computer. The multi-way EEG amplifier first amplifies EEG signals, then the data collection card collects the EEG signals and transmits the signals to the computer, finally the modules in the computer are utilized to conduct preprocessing and segmentation on the EEG signals and calculate the lacuna characteristics of the signals, a Bayes linear discriminant analysis classification device is utilized to classify the EEG lacuna characteristics, and the threshold judgment module is used for marking the classification and obtaining a result. The EEG classification detection device has the advantages of being simple in characteristic operation, high in practice and classification speed, high in classification accuracy and capable of achieving good classification detection effect.

The invention belongs to the field of EEG signal identification and artificial intelligence, and particularly relates to a motion imagination EEG classification processing method based on a sparse representation classification algorithm. The invention includes that a subject wears a scalp electrode cap with wireless or wired transmission on head in an environment with less external interference and imagines the movements based on the prompting, the EEG cap detects the EEG signals of the subject, and then the EEG signals are subjected to preliminary bandpass filtering, and the filtered EEG signals are imported to a host computer to be stored; and the host computer software organizes and marks the imported original EEG signals and makes a sample set and a test set for classification. According to the invention, only a large dictionary matrix is calculated and constructed once, the subsequent processing is calculated by means of sparse representation coefficient matrix, so that the data size of multi-channel motion imagination EEG information is greatly reduced, the data calculation pressure is reduced, and the operation speed is improved.

The invention discloses an EMD and gaussian kernel function SVM-based EEG emotion classification method. Aiming at the problem that the accuracy of EEG signal classification is not high, an Empirical Mode Decomposition (EMD) technology and SVMs are combined, first EMD is performed on an EEG signal to obtain a plurality of modal components, each modal component contains effective information of different frequencies, then frequency energy is used as a quantitative criterion of each modal component, i.e., each EEG signal can obtain different characteristic values, and the characteristic values are used as characteristic values of an EEG sequence to perform a next step of sample value classification. Experiments show that the EMD and gaussian kernel function SVM-based EEG classification method can improve the accuracy of EEG signal classification.

The invention relates to a prior knowledge-based multi-modal ethnic identity sense quantitative measurement method and belongs to the intelligent pattern recognition and ethnology research crossing filed. According to the measurement method, ethnic minority emotion pictures are adopted as basic materials; ethnic minority identity sense survey questionnaire questions are converted into the induction materials of electrophysiological signals; after the EEG information of subjects is acquired, the EEG information implying identity senses is classified through a fuzzy neural network learning-basedEEG emotion feature extraction and classification algorithm; questionnaire survey results and EEG classification results are fused, so that ethnic identity sense quantitative measurement is realized;and the objective feeling of the subjects for the ethnic identity sense and national identity sense can be automatically analyzed. With the method of the invention adopted, the subjective influence of questionnaire surveys can be decreased, the objectivity of identity sense classification can be enhanced, and ethnic identity sense quantitative classification is more accurate and more efficient.

The invention proposes an EEG classification method based on a Fisher discrimination sparse extreme learning machine. The method comprises the steps: firstly training a structural dictionary through aFisher criterion; secondly obtaining a more discriminative sparse coefficient according to the dictionary, and obtaining a more effective feature signal; finally carrying out the classification of anew feature signal through an extreme learning machine algorithm, thereby improving the accuracy of multi-motion imaginary task classification. The method is good in application prospect in the fieldof brain-machine interfaces.

The invention discloses a multi-modal electroencephalogram classification method under multi-source interference based on a multi-head attention mechanism, and relates to the technical field of electroencephalogram signal processing. The method comprises the following steps: synchronously acquiring three types of electroencephalogram modal signals of motor imagery, steady-state visual evoked and event-related potentials, and synchronously acquiring multi-source interference data; performing data preprocessing on the multi-mode signal data; time domain, frequency domain and spatial domain three-dimensional features of each electroencephalogram mode are extracted, and meanwhile, physical features of interference data are quantified; the multi-domain features of the three types of electroencephalogram modes serve as parallel query vectors, the multi-source interference features serve as key value vectors, interference suppression weights are dynamically distributed through a layered attention mechanism, and anti-interference enhanced multi-mode fusion features are generated; and the multi-modal fusion features are processed through a gating circulation unit and a time domain attention module, a multi-modal classification result is output, and online learning and weight updating are carried out. According to the method, a robust decoding scheme can be provided for a brain control interaction system in a complex environment.

The invention relates to an electroencephalogram classification method fusing phase amplitude coupling characteristics, and belongs to the field of electroencephalogram signal processing and deep learning. The method comprises the following steps: collecting electroencephalogram signal data, and carrying out channel selection and preprocessing; performing Hilbert transform on the EEG signal of each channel, and extracting instantaneous phase and amplitude information; calculating the coupling degree of phases and amplitudes between different frequency bands, and generating a phase-amplitude coupling characteristic matrix for representing the cross-frequency coupling strength; and the PAC features and the original EEG time sequence features are jointly input into an improved Transform classification network, a self-attention mechanism is utilized to effectively model a time sequence dependence and frequency coupling relationship, multi-scale time domain, frequency domain and space features are extracted, fusion calculation is performed, and a final classification result is output. According to the method, the dependence of a traditional electroencephalogram classification method on a single frequency spectrum feature is overcome, the modeling capability of an electroencephalogram activity cross-frequency cooperation mechanism is enhanced, and the accuracy and generalization performance of electroencephalogram classification are improved.

The invention discloses a brain function network feature extraction method based on a dynamic directional transfer function. The method mainly comprises the steps of firstly, performing preprocessingsuch as common average reference and lead optimization on an original motor imagery electroencephalogram signal; secondly, calculating a network connection edge of the preprocessed electroencephalogram signal by adopting a proposed DDTF algorithm, and respectively constructing brain function networks of different frequency bands; calculating network characteristic parameter outflow information andinformation flow gain according to the brain function network, and fusing two characteristic parameters in series to serve as characteristic vectors to be sent into a support vector machine for characteristic evaluation; and finally, determining an optimal parameter and an optimal frequency band according to a recognition rate closed loop to obtain a final classification result. The method is used for constructing the motor imagery brain function network, the network parameters obtained through calculation are used for MI-EEG feature extraction, the method not only can accurately describe change characteristics of MI-EEG in a frequency domain, but also accurately reflect a dynamic evolution process of BFN, and improvement of the MI-EEG classification accuracy is greatly facilitated.

The invention discloses a disturbance of consciousness electroencephalogram discrimination method and system adaptive to channel deletion. According to the method, on the basis of an electroencephalogram classification model of a deep network, automatic discrimination of disturbance of consciousness (MCS and UWS) is carried out with high accuracy according to resting-state electroencephalogram energy of a patient. The model utilizes a time domain double-branch CNN module and a space-time Transform module to learn features in electroencephalogram data hierarchically; and a model architecture with variable channel dimensions is introduced, an inter-channel association learning method fusing electrode position information and a training strategy of channel random discarding are introduced, so that the model can adapt to a scene in which part of channels are missing, and the clinical applicability of the method is improved.

The invention discloses a symbol transfer entropy and brain network feature calculation method based on time-frequency energy, which comprises the following steps: firstly, preprocessing collected motor imagery electroencephalogram signals (MI-EEG) based on common average reference; then, continuous wavelet transform is carried out on each lead MI-EEG, a time-frequency-energy matrix of each lead MI-EEG is obtained, time-energy sequences corresponding to each frequency in a frequency band closely related to motor imagery are spliced in sequence, and a one-dimensional time-frequency energy sequence of the lead is obtained; further, symbol transfer entropy between any two lead time-frequency energy sequences is calculated, a brain connectivity matrix is constructed, and matrix elements are optimized by using a Pearson feature selection algorithm; and finally, calculating the degree and the middle centrality of the brain function network, and forming a feature vector for MI-EEG classification. The result shows that the frequency domain feature and the nonlinear feature of the MI-EEG can be effectively extracted, and compared with a traditional feature extraction method based on the brain function network, the method has obvious advantages.

The invention provides a machine learning model robustness evaluation method based on noise data. The method comprises the steps of original data set processing, noise data acquisition, model training, model prediction, accuracy reduction ratio calculation and model robustness evaluation. The original data set processing comprises the steps of collecting an original data set with a correct percentage label, and dividing an original training set and an original test set by adopting 10 times of 10-fold cross validation. The noise data acquisition comprises the following steps: on the basis of anoriginal training set, extracting t'= | D | * alpha data by adopting a stratified sampling method, and replacing a label of the data with an error label, and alpha is a noise data rate. Model training comprises the step of respectively inputting an original training set and a training set mixed with noise data to respectively construct an original model and a new model based on a common classification algorithm. Model prediction includes performing accuracy evaluation on an original model and a new model based on an original test set. Accuracy decline ratio calculation includes calculating arate of decline in accuracy of the new model relative to the original model. Model robustness evaluation comprises the steps of comparing the size of the rate of accuracy reduction in the transverse direction and the longitudinal direction, measuring the robustness of the model, and achieving the standard of judging the robustness of the model.

Embodiments of the present disclosure provide a target recognition method based on an electroencephalogram (EEG) signal in a natural reading environment. The method includes steps 1-6. Step 1 includes determining a fuzzy semantic target recognition paradigm by selecting a stimulus material and designing an experimental Block. Step 2 includes performing an EEG experiment and acquiring the EEG signal according to the fuzzy semantic target recognition paradigm. Step 3 includes assessing quality of the acquired EEG signal and constructing an EEG database by combining a corresponding label. Step 4 includes obtaining a preprocessed EEG signal by performing preprocessing on the EEG signal in the EEG database. Step 5 includes performing feature extraction on the preprocessed EEG signal. Step 6 includes establishing an EEG classification model, and training and testing the established EEG classification model to recognize and classify a fuzzy semantic target in the natural reading environment.

The invention discloses a cross-subject movement phenomenon electroencephalogram signal classification method based on TSLANet and Riemannian geometric features, and relates to the technical field of electroencephalogram signals. The classification method is a new motor imagery electroencephalogram classification method based on a deep learning model, the method comprises the steps that firstly, original electroencephalogram signals are subjected to filtering and normalization processing to serve as input of the novel deep learning model RMETNet, the RMETNet combines a convolutional network and a TSLANet network, complex time sequence features are extracted through the TSLANet, and then the complex time sequence features are extracted through the deep learning model RMETNet; meanwhile, Riemannian geometric features are learned through a multi-scale convolution module, and the RMETNet combines time sequence features with features after Riemannian geometric alignment, so that feature expression is enriched, the accuracy of motor imagery electroencephalogram classification is improved, in order to reduce cross-subject distribution feature differences, MMD loss is introduced in the training process of the RMETNet, and the accuracy of motor imagery electroencephalogram classification is improved. The distribution consistency between the source domain and the target domain is enhanced, so that the effect of the cross-subject experiment is improved, the superiority of the proposed model is evaluated by carrying out the single-subject experiment and the cross-subject experiment on the two common data sets, and a good result is obtained.

The invention relates to a cognitive state classification method based on EEG-fNIRS spatio-temporal fusion features, which comprises the following steps of: setting an experiment according to a mental calculation experiment normal form, and synchronously acquiring EEG data and fNIRS data of a tested mental calculation task; preprocessing the two collected data to obtain electroencephalogram signal data and hemoglobin concentration change data; eEG signal data and hemoglobin concentration change data are taken, data enhancement is carried out through a time window, then the data are sent to a space-time fusion network, EEG features, oxyhemoglobin HBO features and deoxyhemoglobin HBR features are obtained, fusion features are obtained through fusion, and an EEG classification result, an fNIRS classification result and a fusion feature classification result are obtained. According to the method, the characteristics of the EEG signal and the fNIRS signal are effectively combined, the overfitting problem in modal deep learning is relieved, and cognitive state classification can be accurately carried out.

The invention discloses a video awakening degree classification method and device and a computer device, wherein the classification method comprises the following steps: step 1, collecting an EEG signal when a tester watches a video, and obtaining EEG data; step 2: standardizing the obtained EEG data according to columns; step 3, carrying out feature selection on the standardized EEG data to obtain EEG classification features; step 4, classifying the video according to the obtained EEG classification features. The embodiment of the invention classifies the video according to the EEG classification features by standardizing the collected data, and then extracts the EEG classification features from the EEG signals by using the SBMLR algorithm for the standardized processed data, thereby improving the accuracy of the awakening degree classification of the video.

The invention relates to an EEG (electroencephalogram) classification method adaptive to different sampling frequencies, and relates to a signal classification method. The EEG classification method adaptive to different sampling frequencies includes the steps: 1) construction of a CNN-E classification model based on a convolutional neural network; and 2) training and testing method for sample dataof different lengths. The model can be used to learn and classify EEG signals at different sampling frequencies, and can be adaptive to signals of different lengths. The model and a traditional classification method based on feature extraction can analyze the possible problems in the classification of EEG signals at different sampling frequencies. The network model CNN-E can be adaptive to various lengths of data by means of autonomous learning of the sample data, and by means of a simple and effective completion method. The experimental results show that the network model CNN-E can achieve well classification effect and preferable universality for classification of EEG signals at the same sampling frequency, classification of EEG signals at different sampling frequencies, and classification of EEG signals with different sample lengths.

The application discloses a method for classifying epilepsy electroencephalogram (EEG) based on an iterative graph convolutional neural network, calculates the node similarity and distance similarity of epilepsy EEG data as an original graph structure, introduces a multi-head graph attention mechanism for node similarity measurement learning, iteratively optimizes the parameters of the graph structure and the graph convolutional neural network, finds the optimal graph structure and achieves the optimal epilepsy EEG classification effect. Experiments are conducted on TUEP bipolar and unipolar montage datasets and TUAB and MPI LEMON joint datasets to verify the effectiveness of the method. The method has better epilepsy EEG classification effect and obtains more accurate electroencephalogram structure.

The invention discloses a motor imagery EEG classification method based on HELM combined with PTSNE manifold and LDA feature fusion, and improves the classification accuracy. In terms of feature extraction, on the one hand, using PCA combined with LDA to extract linear features can not only eliminate noise, but also consider the label information of training data; Complex nonlinear intrinsic manifold features. In terms of feature classification, the HELM algorithm with high classification accuracy is used to classify and identify motor imagery EEG signals.

The invention discloses an MI-EEG classification and recognition method based on a pulse neural network. The method comprises the steps that electroencephalogram signals are collected and preprocessed; converting the two-dimensional time-frequency image into a pulse sequence in a Poisson coding mode; generating a frequency histogram of excitation neuron pulse distribution by inputting the pulse sequence into a pulse neural network; and classifying the output pulses by adopting a voting method. According to the invention, the pulse neural network is used to identify and classify the image, so that the precision meets the requirement, and the expenditure of power consumption and the like is reduced; meanwhile, an STDP learning mechanism and a lateral inhibition mechanism are introduced, the connection strength is adjusted through the STDP learning mechanism, and excitation neurons of unissued pulses are inhibited through the lateral inhibition mechanism; the STDP learning mechanism and the side suppression mechanism jointly influence the neuron group, so that the neurons of the corresponding instructions can emit pulses more easily, the membrane potential of the inactive neurons is reduced, the cost of emitting the pulses is increased, and the pulses are less likely to be emitted.

The invention provides an electroencephalogram signal classification method based on pre-training language model reprogramming, which belongs to the field of electroencephalogram signal processing and comprises the following steps: 1, preprocessing original electroencephalogram signals in a data set; 2, establishing an electroencephalogram signal prediction network, performing cross-modal fusion on the electroencephalogram signals and the text prototype to form fusion representation, inputting the fusion representation into a pre-trained large language model for reasoning, and outputting feature attributes of future electroencephalogram signals through a prediction module; 3, performing model pre-training through a single-channel self-supervision strategy in a model pre-training stage; and 4, according to the downstream electroencephalogram signal classification task, replacing the prediction module with a classifier module to carry out fine tuning training so as to adapt to different electroencephalogram classification tasks. Compared with an existing electroencephalogram signal classification method, the electroencephalogram signal classification method has good generalization ability and can adapt to data of different channel formats, and therefore the application value of electroencephalogram signals in the fields of medical treatment and the like is improved.

An emotion recognition system and application based on deep learning and brain-computer interface, comprising a sequentially connected portable EEG acquisition device, an emotional EEG classification module and an emotion classification display module, the portable EEG acquisition device collects data from the brain of a subject Emotional EEG signal, the emotional EEG classification module analyzes the emotional EEG signal, establishes a relationship model between the emotional EEG signal and emotion, and through the relationship model, the input emotional EEG signal is analyzed. Emotional classification, the emotional classification display module displays the recognized emotional types and prompts the emotional state of the subject. The emotion recognition system and application based on deep learning and brain-computer interface of the present invention can realize accurate acquisition, effective identification and correct classification of emotional EEG signals, intuitively prompt emotional states, and realize the function of emotional state monitoring. The invention directly establishes the corresponding relationship between the EEG signal and the emotion type, and realizes the feedback to the emotion.

The invention discloses an electroencephalogram signal continuous learning classification method and system based on similarity perception playback, and relates to the crossing field of artificial intelligence and neural engineering technologies. The method is characterized in that EEG data stream segments are continuously input into a trained electroencephalogram classification model in an incremental mode, and dynamic adaptation of personalized electroencephalogram signals is achieved; the training process of the classification model comprises the following steps: constructing a time step driven incremental learning framework by adopting a time sequence cross validation strategy, and constructing a training sample set of each time step; a deep learning model is constructed by using deep convolution and separable convolution, the deep learning model is trained based on the training sample set of each time step, after training of each time step is finished, an experience pool is updated based on a similarity perception mechanism, and the experience pool is used for storing historical data samples; when the deep learning model is trained based on the training sample set from the time step 2 to the time step T, carrying out joint training on the training sample set of the current time step and historical samples randomly retrieved from the experience pool; constructing a loss function of batch training, and optimizing trainable parameters in the classification model; according to the continuous learning classification method and system, the dynamic adaptive capacity of the EEG classification model to new data is remarkably enhanced, and high efficiency and accuracy are kept in a continuously changing clinical environment.

A target recognition method based on an electroencephalogram (EEG) signal in a natural reading environment is provided, including: presenting a fuzzy semantic target to a subject through a user interaction window on a display device; acquiring EEG signals of the subject via a wireless EEG acquisition device to obtain a target EEG signal corresponding to the fuzzy semantic target; determining a binary classification result corresponding to the fuzzy semantic target based on the target EEG signal through a trained EEG classification model; in response to the binary classification result indicating that the fuzzy semantic target is recognized, determining a semantic category to which the fuzzy semantic target belongs; and controlling, based on the semantic category, the display device to highlight annotations on the user interaction window; wherein highlighting annotations includes highlighting a text or an image related to the fuzzy semantic target and displaying associated information of the semantic category.

The invention discloses an electroencephalogram signal classification method based on a domain self-adaptive data synthesis contrast diffusion model, which comprises the following steps of: constructing the domain self-adaptive data synthesis contrast diffusion model which comprises a forward diffusion process and a backward diffusion process; the forward diffusion process is used for training noise-containing data; a comparison denoising module is arranged in the back diffusion process, and the comparison denoising module is used for effectively learning clean signals and domain noise in the back diffusion process to generate clean data; and finally, the obtained clean signals are sent to a standard classification network EEGNET for classification. The domain adaptive data synthesis contrast diffusion model provided by the invention is more excellent in performance in an electroencephalogram classification task, and highlights the high efficiency of the model in the aspect of improving the reliability of data synthesis. According to the method, fine feature changes in the electroencephalogram signals can be effectively captured, and personalized noise can be learned according to different tasks.

The invention belongs to the technical field of electroencephalogram signal classification, and relates to a training method and application of an electroencephalogram signal classification model with weak asymmetric visual stimulation. The training method comprises the steps of obtaining a frequency domain positive frequency signal data set based on an original signal and dividing the data set into a training set, a test set and a verification set; constructing an electroencephalogram signal classification model, and initializing a mode decomposition matrix of a propagable gradient; the training of the model comprises the following steps: dividing a training set into at least two mutually exclusive subsets, updating a modal decomposition matrix by utilizing back propagation iteration of center frequency band loss, bandwidth loss, reconstruction loss and classification loss of each mutually exclusive subset, and updating parameters of a convolutional neural network and a full connection layer of the model by utilizing back propagation iteration of the classification loss. And verifying the discrimination performance of the model by using the verification set, storing the model parameter with the optimal discrimination performance, and evaluating the model with the optimal discrimination performance parameter by using the test set. According to the method, the discrimination performance in an electroencephalogram classification task of weak asymmetric visual stimulation can be remarkably improved.

The application discloses a kind of based on fast sequence visual presentation electroencephalogram classification method of spatiotemporal progressive attention model, comprising: obtaining the electroencephalogram data to be classified;The electroencephalogram data to be classified is input to the trained spatiotemporal progressive attention model and is processed, obtains the category of electroencephalogram data to be classified;Wherein, the trained spatiotemporal progressive attention model with the preset data after acquisition and processing as training data set, to extract the spatial feature and time feature of electroencephalogram data as the purpose, after training to initial spatiotemporal progressive attention model, obtain.The electroencephalogram data is obtained by collecting subject in the mode of fast sequence visual presentation and watching image sequence, and the image sequence includes infrared small target image and non-target image.The present application can maximize the use of electrode and time segment useful information, while minimizing irrelevant information.

The present invention discloses a method for classifying depressive electroencephalogram based on a dual-branch fusion model in deep learning, comprising the following steps: (1) obtaining electroencephalogram signals of the Fp1, Fpz, and Fp2 electrodes in the prefrontal lobe of the brains of a number of healthy individuals, (2) obtaining electroencephalogram signals of the Fp1, Fpz, and Fp2 electrodes in the prefrontal lobe of the brains of a number of patients with mild depression, (3) obtaining electroencephalogram signals of the Fp1, Fpz, and Fp2 electrodes in the prefrontal lobe of the brains of a number of patients with moderate depression, (4) training and learning the dual-branch fusion model with the input forms of healthy controls, patients with mild depression, and patients with moderate depression in steps (1), (2), and (3), and (5) converting the electroencephalogram signals of the window to be analyzed into corresponding wavelet time-frequency diagrams and inputting them into the dual-branch fusion model trained in step (4) to complete the analysis of the electroencephalogram signals. This method has good effects and can distinguish between depression and health as well as the degree of depression.

The invention discloses a motor imagery EEG classification method based on the sparse representation of space-time-frequency optimized features, which mainly uses linear discriminant criteria to select the most favorable lead, time segment and frequency segment for classification, and extracts EEG features through a co-space pattern algorithm. Finally, classification is performed according to the feature sparse representation. The invention includes electroencephalogram signal preprocessing, lead selection, time-frequency block selection, feature extraction and feature classification. The results show that the method of the present invention can effectively select the most favorable lead, time segment and frequency segment for classification, and the sparse representation of the features extracted by the co-space pattern algorithm can achieve better classification effect. Compared with the existing algorithms, this method can automatically select the most beneficial space-time-frequency parameters for classification, and combine the features in the optimal time-frequency block, which is conducive to improving the accuracy of motor imagery EEG signal classification.

The present invention is grading method for establishing the response of EEG files to physiological emotions through human factor lighting (HCL) system and EEG includes the following steps: Step 1, enhance spectrum which obtained the specific color temperature with synergistic effect on the specific physiological emotional response identified by fMRI; Step 2, making the user wear an EEG and give the element with specific emotional stimulation to stimulate the user's specific physiological emotion and induced the user's specific physiological emotion; Step 3, carry out the lighting program, after step 2, start the HCL system to lighting the user with different color temperatures, record and store the EEG files after lighting with different color temperatures; Step 4, EEG files that classify specific emotions by similarity through the learning method of AI; Step 5, establish the EEG classification database according to the similarity ranking of EEG files with specific color temperature.