EEG Feature Analysis of Motor Imagery Based on Lempel-Ziv Complexity at Multi-scale

Electroencephalogram(EEG)feature extraction of motor imagery is an important issue in the field of brain-computer interfaces.In this paper,an algorithm of EEG feature extraction of motor imagery based on Lempel-Ziv complexity at multi-scale is put forward.This algorithm is the improvement compared to that of the traditional binary quantification Lempel-Ziv complexity,while it divides the EEG into several areas with different amplitude.The Lempel-Ziv complexity can be obtained from the binary quantification of EEG according to the rise and decline trends of it in different areas.This paper extracts Lempel-Ziv complexity from different typical time interval,separates EEG of motor imagery into four areas and finally uses the Support Vector Machine to classify.The experimental result shows that the average classification accuracy of EEG of two hands motor imagery can reach 87.87% to the highest owing to the classification method featured by Lempel-Ziv Complexity at Multi-scale,which excels the algorithm of traditional binary quantification Lempel-Ziv complexity.