Feature Selection and Classification of EEG Finger Movement Based on Genetic Algorithm

Abstract

Electroencephalography (EEG) classification for mental tasks is the crucial part of the brain-computer interface. Many studies try to extract discriminative features from EEG signals. In this study feature selection algorithm based on genetic algorithm (GA) was implemented to find the best features that describe EEG signal. The best features are searched among ten statistical features calculated from the cross-correlation of effective channel with relevant EEG channels in the proposed study. A comparison was made after and before feature selection in two major viewpoints: classification accuracy and computation time. Multi-Layer Perceptron Neural Network (MLP) and Support Vector Machine (SVM) are used to classify left and right finger movements of 13 subjects. The overall classification performance is enhanced about 1% for both classifiers after feature selection. Computation time has reduced about 34% in SVM classifier and there is huge reduction about 84% in MLP.