Statistical Wavelets With Harmony Search- Based Optimal Feature Selection of EEG Signals for Motor Imagery Classification

Abstract

Brain Computer Interface (BCI) does not only help people of physical disability but also being used popularly in several other applications. Motor Imagery (MI) classification is one of the major contributions in BCI which works on segment of EEG signal within particular frequency band. Herein, feature selection plays important role in obtaining good classification results. In this article, Beta and Gamma frequency are considered with Statistical DWT (SDWT) based features for classification of EEG signals (MI classification) for patient monitoring, assistance healthcare services and daily living activities. Harmony search algorithm of feature selection is used to obtain the optimal feature set for classification of MI. The results show that frequency centric SDWT achieves average accuracy of 92.49% for weighted KNN (K-Nearest Neighbour) method. Comparison of accuracies before and after feature selection portrays that feature selection with harmony search improves the performance of proposed MI classification.