Multi-class motor imagery brain electrical signal classification method based on phase synchronization

Abstract

The invention relates to a multi-class motor imagery brain electrical signal classification method based on phase synchronization. According to the method, firstly, phase synchronization features of a training sample and a test sample are calculated respectively through a phase locking value; secondly, correlation coefficients of the training sample and the test sample are calculated and arrayed from large to small after an average value is removed and an absolute value is obtained; thirdly, brain electrical signals are roughly classified according to the arrayed correlation coefficients, and then disaggregated classification is conducted according to the brain electrical signals which are roughly classified, wherein the process is involved in a shared airspace mode feature extraction method and a linear discriminant analysis and classification method. The method comprises the steps of brain electrical signal collection, data pre-processing, filtering, calculation of the correlation coefficients of the phase synchronization features, feature extraction and classification and classification accuracy calculation. Classification results show that by the adoption of the brain electrical signal classification method based on phase synchronization, the classification results are good, the rough class where the test sample belongs can be efficiently and accurately determined through brain electrical signal rough classification based on phase synchronization, and the calculated amount is reduced.

Description

technical field [0001] The invention belongs to the field of electroencephalogram signal classification in the field of biological feature recognition, and specifically relates to a method for obtaining phase synchronization feature correlation coefficients based on electroencephalogram phase synchronization and applying them to a common-space domain mode algorithm to classify multi-category motor imagery electroencephalogram signals. Background technique [0002] For patients with severe loss of function of the neuromuscular system, a new means of communication with the outside world is urgently needed. Brain-Computer Interface (BCI) is just such a technology that allows users to communicate autonomously with the external environment directly through ideas instead of traditional neuromuscular channels. In recent years, the research on brain-computer interface (BCI) has gradually developed from two-category pattern recognition to multi-category pattern recognition, so as to ...

AI Technical Summary

Problems solved by technology

However, the traditional multi-category EEG signal classification methods, such as the method based on the "one-to-one" common space mode, the approximate joint diagonalization method based on the Jacobi algorithm, etc., have the disadvantages of large amount of calculation.

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