30 results about "Eeg classification" patented technology

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 belongs to the field of mode recognition and a brain-machine interface and discloses a motor imagery electroencephalogram voting strategy sorting method based on extreme learning machines. The motor imagery electroencephalogram voting strategy sorting method comprises the following steps: dividing an original motor imagery electroencephalogram into S sections of sub-signals; carrying out dimensionality reduction on each section of sub-signal by a principal component analysis method; carrying out secondary dimensionality reduction on a feature vector subjected to the dimensionality reduction by a linear discrimination analysis method; carrying out the same processing on the S sections of sub-signals to finally obtain S (K-1)-dimensional feature vectors, and combining the S (K-1)-dimensional feature vectors to finally obtain an S*(K-1)-dimensional feature; and transmitting the S*(K-1)-dimensional feature into a plurality of ELM (Extreme Learning Machine) sorting devices so as to obtain a final sorting result by utilizing a voting sorting strategy. The invention provides a voting sorting strategy based on the ELMs; compared with a traditional multi-time ELM average accuracy scheme, the method provided by the invention has the advantages that the sorting accuracy is improved under the condition of not influencing the training sorting low consumption.

The present invention relates to the algorithm of brain-computer interface, artificial intelligence and pattern recognition, more specifically, relates to the SSVEP EEG classification method based on the convolutional neural model enhanced by EMD data, which can be applied to the fields of medical equipment, human-computer interaction, robot control, etc. . This method first preprocesses the original EEG data. After preprocessing, the empirical mode decomposition method is used to decompose the original EEG data, and a large number of artificial EEG data that conform to the time-frequency domain characteristics of the original EEG signal are generated by mixing. The original EEG data is merged and used for parameter training of the neural network, so as to achieve the effect of effectively training network parameters with a small amount of EEG data. Finally, the complex Morlet wavelet transform is used to generate the EEG tensor, and the original time domain data is converted into a tensor dictionary integrating time domain, frequency domain and spatial information as the input of the neural network, and the convolutional neural network model is used to enhance the data. EEG training set for classification.

The present application provides a method for generating an EEG classification model, a device for generating an EEG classification model, electronic equipment, and a computer-readable storage medium. The method for generating an EEG classification model includes: obtaining sample data of K subjects, wherein, The sample data includes: classified EEG information and classification results of the corresponding EEG information, the K is greater than or equal to 2; based on the sample data of K subjects and the preset first objective function, the calculation makes the first An orthogonal transformation matrix with the minimum value of the objective function, wherein the first objective function is a function related to the orthogonal transformation matrix and the EEG information of K test subjects, and the orthogonal transformation matrix is ​​used to transform the K test subjects The respective EEG information is transformed into correlation information among K subjects; an EEG classification model is generated based on the orthogonal transformation matrix. The technical solution of the present application is used to generate an EEG classification model that can be applied to multiple subjects, saving the maintenance cost of the EEG classification model.

The invention discloses an EEG classification method based on the entropy value of dynamic functional connection. First, the obtained original EEG signals are preprocessed, and then filtered; then the phase synchronization analysis method is used to calculate the EEG signals of each frequency band at each time. Point the phase relationship between each two channels to obtain the dynamic functional connection matrix; then calculate the time domain entropy of the phase relationship value between the two channels one by one to obtain the information entropy of each edge to measure the time of each edge of the EEG functional network The complexity of the domain; the dynamic functional connection entropy of each frequency band is used as the classification feature of the EEG functional network, and the adaptive improvement classifier is trained to obtain multiple adaptive improvement classifiers and the corresponding classification accuracy; The samples are combined for classification. The problem of low classification accuracy in the existing EEG signal classification methods is solved.

An EEG classification method based on frequency-band attention residual network belongs to the field of pattern recognition and bioinformatics. First, preprocess the original EEG signal to remove noise and artifacts in the signal; then, use a fixed-length sliding window to segment the preprocessed N-lead EEG signal to obtain a total of S-segment N-lead sub-signals ; Then use the wavelet packet decomposition to decompose and reconstruct the S segment N derivation sub-signals, each sub-band signal of each data segment is decomposed into F sub-band signals; after that, the multi-conductor decomposition results of each frequency band are converted into electrode correlation matrix; and then use the electrode correlation matrix of F frequency bands as the input of the frequency band attention residual network to complete the classification of EEG signals. Compared with the existing technology, the present invention has the advantages that the frequency band attention module is used to obtain frequency band importance weights, and a personalized frequency band attention distribution is given to each sample.

The invention discloses a transfer learning method and system for EEG classification based on Euclidean alignment and Procrustes analysis, and belongs to the field of brain-computer interface based on motor imagery. Including: covariance alignment of the feature matrix of previous users and new users; mean alignment of feature matrices of previous users and new users; according to the feature matrix of previous users after the label and mean alignment of previous users, calculate the category center of previous users, Calculate the category center of the new user according to the pseudo-label of the new user and the feature matrix of the new user after the mean value alignment; construct the matrix of the category center of the previous user and the new user, and calculate the relationship between the category center of the new user and the new user through the orthogonal Platts analysis method. The rotation matrix corresponding to the center alignment of the categories of the previous users; the feature matrix of the new user after the mean alignment is multiplied by the rotation matrix to obtain the final aligned new user data.