11 results about "Common spatial pattern" patented technology

The invention discloses an online brain-computer interface method for enhancing hand fine motion intention recognition performance based on a multi-parameter coupling feature modulation technology. According to the method, a kinematics parameter (slow / fast), a visual auxiliary stimulation parameter (dynamic / static visual auxiliary stimulation) and different action types are introduced to enhance the separability characteristic difference of the hand fine motion intention. An online system extracts sub-band features and constructs feature vectors by using event-related desynchronization features generated by fine motor imagery through a common spatial pattern method of multi-frequency spatial filtering, and then performs fine intention recognition on the extracted features by using a Riemannian mean minimum distance classifier. And an identification result is fed back to the subject in a picture form. According to the method, the problem that the feature separability is not obvious due to the fact that the spatial resolution of the hand fine motor imagery electroencephalogram feature is low is effectively solved, the recognition accuracy of the hand fine motor intention is remarkably improved, and a motor imagery brain-computer interface instruction set is expanded.

The invention discloses a driving electroencephalogram signal recognition method based on a multi-domain asymptotic convolutional neural network, and relates to the field of electroencephalogram intelligent recognition. The method mainly comprises the steps of electroencephalogram (EEG) acquisition, frequency domain feature and spatial feature extraction, and variable asymptotic convolutional neural network modeling, recognition and classification. After the collected EEG signals are preprocessed, frequency domain features are obtained through fast Fourier transform (FFT) processing, and spatial features are obtained through common spatial pattern (CSP) processing. All frequency band features are stacked, an input matrix is constructed and input to the constructed variable asymptotic convolutional neural network model, strategies used in different stages are different, and recognition classification about five driving behaviors is obtained. According to the method, the frequency domain-space features and the variable asymptotic convolutional neural network model are combined, and the global features of the EEG signals are more concerned, so that the model classification capability is remarkably improved, and the EEG signal classification and recognition effect is further improved. Meanwhile, the characteristic of light weight of the model meets the requirement of intelligent driving on real-time performance.

The present invention discloses a method for decoding imagined movements based on tactile sensation calibration, comprising: (1) setting up a tactile stimulation device and applying tactile stimulation to the dorsal side of the left or right wrist of the subject; (2) setting up an EEG device to record EEG signals, with the original signals being digitally sampled at a frequency of 500 Hz and using a filter to filter the original signals; (3) using the tactile stimulation device and the EEG device to collect the pure imagined movement data of the subject, as well as the imagined movement data assisted by tactile sensation; (4) using the common spatial pattern algorithm to extract the EEG data features and training a classifier for classifying the EEG data features; wherein, the imagined movement data assisted by tactile sensation is used as the training set; (5) applying the trained classifier to pure imagined movement decoding. By using the present invention, EEG data with a higher signal-to-noise ratio can be obtained, and then a more robust and accurate model can be trained for movement decoding.

The present invention discloses a method for recognizing motor imagery gestures based on the multimodal fusion of electroencephalogram (EEG) and electromyogram (EMG). It uses FBCSP to extract features from EEG. First, a filter bank is used to decompose the filtered EEG signals to extract signal features in different frequency bands. For each frequency band, the common spatial pattern analysis method is adopted. By finding a pair of projection matrices, the signals are projected from the original space to a new space, so that the variance of different categories of EEG signals in the new space is maximized or minimized. The EMG signals of two channels, namely the anterior group and the posterior group of the forearm muscles, are extracted. After preprocessing the signals, the mean values, ranges and the mean ratio of the two channels are extracted respectively. Finally, the EEG features and EMG features are concatenated and fused, and an SVR algorithm is used to train a model and deploy it on the server. The present invention makes full use of the complementarity of information between the multi-bioelectric signals, namely EMG and EEG, and significantly improves the classification effect of motor imagery recognition.

The application provides a small sample electroencephalogram recognition method, system and device based on multi-feature fusion, which comprises the following steps: slicing a to-be-tested electroencephalogram according to a time axis to obtain a to-be-tested data set; performing micro-state feature extraction and common spatial pattern (CSP) feature extraction on each to-be-tested slice in the to-be-tested data set; splicing the micro-state feature and the CSP feature of each to-be-tested slice to form a fusion feature of the current to-be-tested slice; using a pre-trained classifier model to classify the fusion feature of each to-be-tested slice to identify whether each to-be-tested slice belongs to electroencephalogram of a target disease patient; and when the number of to-be-tested slices in the to-be-tested data set that are determined to belong to the electroencephalogram of the target disease patient is greater than a preset proportion of the total number of slices in the to-be-tested data set, the to-be-tested electroencephalogram is recognized as the electroencephalogram of the target disease patient. The application can accurately recognize the electroencephalogram of a depression patient and provide a diagnosis aid for doctors.

The invention relates to the technical field of motor imagery electroencephalogram signal decoding, in particular to a multi-source cross-subject motor imagery electroencephalogram signal classification method based on depth domain adaptation, and aims to solve the problem of large data distribution difference between different subjects, the method comprises the following steps: firstly, preprocessing electroencephalogram signals and aligning Euclidean spatial data; secondly, key features are extracted by adopting a regularization common space mode method, and signal feature representation is optimized; and finally, constructing a deep learning model based on a multi-layer perceptron, and respectively aligning marginal distribution and category information in combination with the central moment difference loss and the contrast domain difference loss to realize high-precision cross-subject classification. The method is suitable for brain-computer interface scenes such as exercise rehabilitation and auxiliary equipment control, and has high robustness and practicability.

The invention discloses an epilepsy signal processing method based on a TSCL technology, and the method comprises the steps: S1, obtaining an EEG signal, and carrying out the preprocessing of the EEG signal, and obtaining a processed EEG signal; s2, respectively adopting a statistical feature extraction method and a common spatial pattern method to carry out time domain feature extraction operation and spatial domain feature extraction operation on the basis of the processed EEG signals so as to obtain time domain features and spatial domain features; s3, performing feature fusion to obtain space-time fusion features, and screening the space-time fusion features; and S4, inputting the screened space-time fusion features into a classifier to obtain an epileptic feature classification processing result, and obtaining epileptic seizure time and epileptic seizure intermission based on the epileptic feature classification processing result. According to the method, the features of the time domain and the space domain are fused, so that more comprehensive feature representation with higher discrimination capability can be generated, meanwhile, the most representative features are automatically screened out, redundant and irrelevant features are eliminated, the prediction complexity is reduced, and the generalization capability is improved.

The present invention relates to a long-term gesture recognition method based on surface electromyography signals. It includes two parts: the first part is data preprocessing, including feature extraction and feature dimensionality reduction; the second part is the adaptive update of the classification model. First is the data preprocessing part. Differential common spatial pattern (DCSP) features are extracted from the gesture data of existing days (usually the data collected on the first day), and then the features are transformed by the nonnegative matrix factorization (NMF) algorithm with activation coefficient normalization. Then is the adaptive update part of the classification model. The first repeated experiment of each test day is used as unlabeled samples, and these samples are evaluated and labeled by the Clustering and classification self-training (CCST) method. The qualified samples, labels and existing data are used to retrain the classification model together. The present invention avoids the trouble of users collecting data for calibration every day and improves the user's comfort level.

The present invention proposes a method for classifying and recognizing group motor imagery based on multi-view decoding of electroencephalogram (EEG). By using common spatial patterns to mine the discriminative spatial features of multi-channel EEG, the spatial view relationship oriented to the intra-individual EEG characteristics is constructed. At the same time, channel selection is carried out based on the Granger causality network, and the coupling information between individuals is extracted through a graph convolutional network to establish a coupling relationship view of group motor imagery EEG. Then, through a multi-view model and a fusion strategy, the advantages of each view are explored to the greatest extent, and a subspace clustering algorithm based on self-representation learning is used to jointly decode the two types of view representation information. By constructing a multi-view representation that combines the spatio-temporal features of single-person motor imagery EEG and the cross-brain coupling features of the group, the spatial view relationship of the intra-individual EEG characteristics is realized, and thus the connections between various channels of multiple brains are fully considered, thereby improving the classification accuracy of EEG motor imagery.

The invention relates to the technical field of brain-computer interfaces, and discloses a lower limb movement brain-computer interface signal acquisition method based on target guidance. According to the method, when a target-oriented lower limb motor imagery task is executed, multi-channel electroencephalogram signals and task event marks are recorded synchronously, sensory motor rhythm frequency band signals are extracted through band-pass filtering, and electro-oculogram and myoelectricity artifacts are removed through independent component analysis. And training a spatial filter by using a common spatial pattern algorithm, and carrying out dimension reduction processing on the signal to obtain an optimal source signal. And by calculating the power value of the optimal source signal in the sensory movement rhythm frequency band, constructing an electroencephalogram feature vector representing the lower limb movement intention. According to the method, the signal purity and the recognition accuracy are improved, and the anti-interference capability of a brain-computer interface system is enhanced.

The method comprises: acquiring an electroencephalogram signal to be classified; performing feature extraction on the electroencephalogram signal to be classified in parallel through a one-to-many filter bank common spatial pattern network and a residual convolutional neural network, and outputting first feature information and second feature information of the electroencephalogram signal to be classified; performing feature relearning on the first feature information and the second feature information in parallel through a dynamic graph convolutional neural network and a gated recurrent neural network, and outputting third feature information and fourth feature information of the electroencephalogram signal to be classified; performing feature fusion on the third feature information and the fourth feature information simultaneously through an attention network to obtain target feature information of the electroencephalogram signal to be classified; and obtaining a target classification result of the electroencephalogram signal to be classified according to the target feature information through a prediction network.