Electroencephalogram signal classifying and recognizing method based on regularized CSP and regularized SRC and electroencephalogram signal remote control system

Abstract

The invention discloses an electroencephalogram signal classifying and recognizing method based on a regularized CSP and a regularized SRC and an electroencephalogram signal remote control system. The method includes the steps of collecting EEG signals produced when n experimenters imagine two different types of movement, and obtaining the covariance of training data of each experimenter, introducing a regularized parameter alpha and a regularized parameter beta (alpha is larger than or equal to zero and beta is smaller than or equal to one), constructing imagine space filters of two different types of movement, reserving the training data after filtering is conducted, extracting maximum vectors of two types of characteristics, constructing a learning dictionary, inputting test movement imagine data, conducting space filtering, and reserving test data after filtering is conducted, recognizing the test movement imagine data through a signal sparse representation method, and determining categories of test samples. The electroencephalogram signal remote control system comprises a signal collecting module, a signal analyzing module and a controller module. According to the electroencephalogram signal classifying and recognizing method based on the regularized CSP and the regularized SRC and the electroencephalogram signal remote control system, the electroencephalogram signals are classified and recognized through the regularized CSP and the regularized SRC, the problem of unstable characteristic extraction is effectively solved, and an electroencephalogram signal classifier has stronger robustness compared with an existing classifier.

Description

Technical field [0001] The invention belongs to the technical field of pattern recognition and intelligent systems and brain-computer interfaces, and in particular relates to a brain electrical signal classification and recognition method based on regularized CSP and SRC and a remote control system thereof. Background technique [0002] At present, there are many diseases that can damage the neuromuscular pathways of the brain to communicate and control with the external environment, such as cerebral palsy, multiple sclerosis and stroke. These diseases can cause people to lose part or all of their autonomous muscle control ability, which requires people Begin to try to establish a new communication and control pathway that does not depend on muscle and nerve activity, that is, to establish an information communication channel between the brain and external devices, so that those with cognitive abilities but with movement disorders can Communicate with the external environment, su...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the embodiments of the present invention is to provide a regularized CSP and SRC-based EEG signal classification and recognition method and its remote control system, aiming at solving the low stability of the eigenvalues ​​of the constructed eigenvectors existing in the existing EEG signal feature extraction , the discrimination degree is relatively poor, and the eigenvector obtained from the recognition classification is difficult to have linear separability, which causes great troubles to the classification and causes the problem of lower recognition rate

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