motor imagery electroencephalogram classification method and system based on Riemannian distance

Abstract

The invention provides a motor imagery electroencephalogram classification method and system based on Riemannian distance. The motor imagery electroencephalogram signal classification method based onthe Riemannian distance comprises the steps of expressing Riemannian space conversion, specifically, motor imagery electroencephalogram vector signals of known category labels by adopting a sample covariance matrix, and acquiring a sample covariance matrix of the known category labels; calculating a Riemannian average value: calculating the Riemannian average value between the sample covariance matrixes of the labels of the known categories to obtain Riemannian average values with the same number as the labels of the known categories; a Riemannian distance calculation step: respectively calculating Riemannian distance values between the sample covariance matrix corresponding to the to-be-classified motor imagery electroencephalogram vector signals and Riemannian average values with the same number of known category labels; and a category output step: taking the category corresponding to the minimum one of the Riemannian distance values as a category label of the motor imagery electroencephalogram vector signal to be classified.

Description

technical field [0001] The disclosure belongs to the field of motor imagery EEG signal classification, and in particular relates to a motor imagery EEG signal classification method and system based on Riemann distance. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] In 1999, the first international brain-computer interface conference was held in New York and the concept of brain-computer interface (BCI, BrainComputer Interface) was proposed. Using brain-computer interface technology, the brain, as the main way for users to communicate and control with the outside world, can better exert their mind control ability. With the development of EEG technology, motor imagery gradually rises. Researchers specialize in the feature extraction of EEG signals of motor imagery and classify and identify the extracted features, which makes the research ...

AI Technical Summary

Problems solved by technology

[0004] The pattern recognition of motor imagery EEG signals in BCI has been done a lot of research at home and abroad. At present, the classification of left and right hand motor imagery EEG features has achieved a high accuracy rate, but the current motor imagery EEG signals It is still immature, and there are still many problems: few types of classification, low classification accuracy, long training time, etc.

The reason for these problems is that in the Euclidean space, the motor imagery EEG signal has a high dimension, feature extraction is difficult, and the difference between different motor imagery is small. Therefore, in order to solve the above problems, it is necessary to jump out of the Euclidean space

[0005] The inventor found that the existing brain-computer interface technology based on multi-task motor imagery has the following difficulties: low classification accuracy, complex algorithm, poor stability, etc.

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