Motor imagery EEG classification method based on sparse representation of space-time-frequency optimized features

Abstract

The invention discloses a motor imagery EEG classification method based on the sparse representation of space-time-frequency optimized features, which mainly uses linear discriminant criteria to select the most favorable lead, time segment and frequency segment for classification, and extracts EEG features through a co-space pattern algorithm. Finally, classification is performed according to the feature sparse representation. The invention includes electroencephalogram signal preprocessing, lead selection, time-frequency block selection, feature extraction and feature classification. The results show that the method of the present invention can effectively select the most favorable lead, time segment and frequency segment for classification, and the sparse representation of the features extracted by the co-space pattern algorithm can achieve better classification effect. Compared with the existing algorithms, this method can automatically select the most beneficial space-time-frequency parameters for classification, and combine the features in the optimal time-frequency block, which is conducive to improving the accuracy of motor imagery EEG signal classification.

Description

technical field [0001] The invention belongs to the field of EEG signal processing and pattern recognition, relates to the classification of motor imagery EEG signals in a brain-computer interface, and particularly relates to a method for classifying motor imagery EEG signals based on sparse representation of space-time-frequency domain optimization features. Background technique [0002] Brain-computer interfaces provide a new communication and control channel between the human brain and external devices, such as computers or prosthetics. By mapping the EEG signals collected by electrodes placed on the head into different control commands, humans can control the actions of external devices through different motor imagery modes. Among various brain-computer interface systems based on EEG, motor-imagination-based brain-computer interface systems have been widely studied because of the potential connection between motor imagery tasks and natural human behavior. Studies have s...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the lack of comprehensive consideration of the space-time-frequency domain in the feature extraction stage in the existing technology, a motor imagery EEG classification method based on the sparse representation of space-time-frequency optimized features is proposed, and the linear discriminant criterion is used to automatically select the most favorable classification method. Leads, time segments, and frequency segments, and extract EEG features through the co-space pattern algorithm to construct an over-complete dictionary matrix, and finally classify according to the linear sparse representation

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