Electroencephalogram channel selection based on pearson correlation coefficient for motor imagery-brain-computer interface

Decryption of Motor Imagery (MI) activity from an Electroencephalogram (EEG) data is a significant part of the Brain-Computer Interface (BCI) technology that allows motor-disabled persons to connect with external devices. Channel selection, feature extraction, and classification are essential requirements for an effective BCI system. Non-stationary EEG data confuses designing EEG-based BCIs. In this study, the Pearson correlation coefficient (PCC) technique is employed for channel selection for EEG signals in the BCI system. It selects the most associated fourteen channels for the sensorimotor area of subject's brain. The popular signal processing technique wavelet packet decomposition (WPD) is employed for feature extraction. After that approximate entropy (ApEn) feature is calculated for selected channels. The proposed study is a novel scheme combining Pearson correlation coefficient-based channel selection technique and wavelet packet decomposition for classifying MI signals. Finally, extracted features are classified with the help of two benchmark techniques, Support Vector Machine (SVM) and K-Nearest Neighbors (K-NN) and achieve maximum accuracy of 91.66% and 90.33%, respectively. The proposed technique is examined on freely available EEG datasets BCI competition-IV-Dataset I to prove its superiority over previously reported approaches. Obtained experimental findings demonstrated advantages over previous methods in terms of classification accuracy.