Abstract
AbstractThe study related to brain–computer interface (BCI) technology is a popular research topic in the present day. The primary objective of a BCI system is to identify the different activities from the recorded electroencephalography (EEG) signal. In the present paper, a four class motor imagery (MI) EEG signal is classified using two feature extraction methods, namely cross-correlation and wavelet energy. The extracted feature vector obtained from the two methods are fed to a linear discriminant analysis (LDA) classifier to obtain the performance accuracy. Binary as well as multiclass classification accuracies are tested through the algorithm. The best average binary class accuracy is obtained as 100%, and the best multiclass average accuracy is 99.86%. After a comparative analysis, it is evident that the wavelet energy method is one of the superior techniques of feature extraction from MI-based EEG data.KeywordsEEG signalBCIWaveletCross-correlationClassification algorithm
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