Research on motor imagery EEG signal processing algorithm

Abstract

To address the difficulty of extracting features from motor imagery EEG signals and the low classification accuracy, a method for feature extraction and classification of multiple types of motor imaging EEG signals based on wavelet and support vector machine (SVM) is proposed. This work first calculated the power of motor imagery EEG data and selected the scale of wavelet packet by theoretical analysis. Then, wavelet packet decomposition on power was discussed, wavelet packet entropy (WPE) of power was calculated, and wavelet packet entropy interpolation of leads C3, C4 was extracted, which composed the feature vector. Finally, this work fed the feature vector as the classifier input into a support vector machine to achieve classification. From Graz’s EEG data from the international BCI competition in 2003, the highest accuracy rate of classification was 97.56%. The feature vectors of this algorithm are low in dimension, are small in data size, and have high classification accuracy, which provides a reference method for the task of EEG feature extraction and classification.