Motor imagery signal classification using Wavelet packet decomposition and modified binary grey wolf optimization

Abstract

Nowadays, Electroencephalogram (EEG) signals are widely used in brain-computer interfaces (BCIs), including the identification of motor imagery (MI) activities and prostheses. In this proposed scheme, wavelet packet decomposition (WPD) is used with approximate entropy (ApEn) as a mechanism for generating features of Electroencephalogram (EEG) signals for the MI-BCI system. Usually, features extracted from an EEG signal include redundant and irrelevant features. This paper proposes a novel scheme of feature selection using Modified Binary grey wolf optimization (MBGWO) for MI-BCI with EEG. Finally, performance of the proposed approach is evaluated using machine learning technique K-nearest Neighbors (KNN). In this study, KNN showed classification accuracy for subjects “a" and “b" is 92.86% and 91.53% respectively. Where “a" and “b" are two participants in Dataset I from BCI competition IV. The results show that the suggested algorithm considerably impacts classification performance. Classification results suggest that the proposed method used in this study can achieve better performance in BCI applications for understanding MI activities in patients with physical disabilities.