Classification Of Motor Imagery Using Pca Features For Brain-Computer Interfaces
A human brain can communicate with the outside world through another proven way that is the brain computer interface (BCI). A BCI simply decodes the electroencephalogram (EEG) signals that are extracted from the brain activities and then sends the command to the concerned device. The device can be any interactive device like a wheelchair. The efficiency of the BCI completely depends on the process of decoding the EEG. In real world the EEG signals are polluted with different artifacts like electromyogram (EMG) and other background activity. This can cause poor decoding activity. In this paper we implement an algorithm that classifies four different motor imagery tasks where one of the body parts is involved: right hand, left hand, tongue and both feet. The electroencephalogram (EEG) data from the BCI Competition IV is used to test the three feature extraction techniques namely: time domain parameters, band power features and PCA features and with two classifiers: support vector machines (SVM) and linear discriminant analysis (LDA). In this combination of methods PCA allows to reduce the number of features and the results show that the combination of feature extraction such as band power, time domain parameters and PCA with SVM classifier assures the better results than in the case of the single feature extraction method.
Keywords: Band Power, Time Domain Parameters, Principal Component Analysis.