Feature Extraction Techniques for the Classification of Four-Class Motor Imagery Based EEG Data: A Comparison

Abstract

AbstractIn this paper, the four-class classification (Left hand, right hand, feet, and tongue) Motor Imagery (MI) signals is performed using four different feature extraction techniques. First, raw EEG signals are pre-processed using the Multi-Class Common Spatial Pattern (CSP) method (one-versus-rest scheme), which discriminates features in feature space and improves the accuracy of classification. Then, four different features, namely channel FFT energy, mean band power, mean channel energy, and Discrete Wavelet Transform (DWT) based mean band energy features, are extracted from pre-processed EEG signals and compared to find the most suitable feature for the discrimination of four-class MI tasks. Besides, three classifiers, namely Bayesian Network classifier (Naive Bays), Linear discriminant analysis (LDA), and Linear Support Vector Machine (SVM), are compared. Performance evaluation is done on BCI competition IV dataset 2a using classification accuracy along with different performance measures calculated from a confusion matrix. The performance of the LDA classifier is found better than linear SVM and Naive Bays. The presented framework with DWT as a feature extraction technique and LDA classifier has obtained an average test classification accuracy of 80.29% over four subjects out of nine. The less computational cost of this framework makes it suitable for online Motor Imagery based BCI Systems.KeywordsElectroencephalogram signals (EEG)Motor imagery (MI)Brain computer interface (BCI)Discrete wavelet transform (DWT)Common spatial pattern (CSP)