Motor imagery EEG pattern recognition method based on time-frequency parameter optimization of artificial bee colony

Abstract

The invention discloses a motor imagery EEG pattern recognition method based on the time-frequency parameter optimization of an artificial bee colony. The method comprises the steps of conducting the leads selection based on the linear decision rule, selecting time-domain and frequency-domain optimal parameters based on the artificial bee colony algorithm, extracting features based on the common spacial pattern algorithm, and finally classifying features based on the linear discriminant analysis algorithm. The result of the method shows that, a lead channel of larger inter-class distinction degree can be effectively selected based on the lead selection algorithm. At the same time, based on the time-frequency parameter optimization algorithm of the artificial bee colony, a time window and a frequency band of larger inter-class distinction degree can be automatically selected, so that a better classification effect is obtained. The method is capable of effectively recognizing different motor imagery modes. Compared with the traditional parameter manual selection method and the frequency-domain parameter automatic selection algorithm, global optimal parameters can be automatically searched in both time domain and frequency domain at the same time based on the above method. Therefore, the feature extraction and feature classification effect for motor imagery EEG signals is improved.

Description

technical field [0001] The invention belongs to the field of electroencephalogram signal pattern recognition, in particular to a motor imagery electroencephalogram pattern recognition method based on artificial bee colony time-frequency parameter optimization. Background technique [0002] After years of exploration and development, the Brain Computer Interface (BCI) technology based on electroencephalography (EEG) has shown its special value in the field of neurorehabilitation. BCI provides another message and control command transmission for humans and machines. aisle. Among the various EEG-based BCI systems, motor imagery-based BCI systems have been extensively studied because of the potential link between motor imagery tasks and natural human behavior. Studies have shown that, similar to the actual action performed by the human body, imagining the movement of a certain part of the human body will also activate a certain area of ​​the human brain's motion perception cort...

AI Technical Summary

Problems solved by technology

The determination of parameters in specific frequency bands and time intervals will directly affect the effect of subsequent feature extraction and feature classification, and existing methods cannot automatically select global optimal parameters in both time intervals and frequency bands.

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