A Study and Performance Analysis of Three Paradigms of Wavelet Coefficients Combinations in Three-Class Motor Imagery Based BCI

Brain computer interface (BCI) provides an interface between a brain and a computer in order to enable people to control external devices without using muscles. In this work, authors report on the results of implementation of three algorithms using wavelet features collected with different kinds of features during imagining left hand, right hand, and foot movements. The features of event-related desynchronization (ERD/ERS) were extracted from alpha and beta frequency bands, and followed by one classifier among the three following ones, linear discriminant analysis (LDA), support vector machine (SVM) or K-nearest neighbor (KNN). The data were recorded from three subjects, provided by BCI-Competition III. The performance evaluation of the proposed algorithms was provided by Matab simulation. The best combination was the wavelet coefficients and common spatial pattern algorithms, followed by the supper vector machine classifier with an average classification accuracy of 75%, which is an interesting for motor imagery application.