Brain-Computer Interface: Feature Extraction and Classification of Motor Imagery-Based Cognitive Tasks

Decoding motor imagery (MI) signals accurately is important for Brain-Computer Interface (BCI) systems for healthcare applications. Electroencephalography (EEG) decoding is a challenging task because of its complexity, and dynamic nature. By improving EEG signal classification, the performance of MI-based BCI can be enhanced. In this paper, five features (Band Power (BP), Approximate Entropy (ApEn), statistical features, wavelet-based features, and Common Spatial Pattern (CSP)) are extracted from EEG signals. For classification, Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Artificial Neural Network (ANN) are used. These methods are tested on a publicly available Physionet motor imagery database. The EEG signals are recorded from 64 channels for 50 subjects, while the subject is performing four different MI tasks. The proposed method achieved an accuracy of 98.53% for left and right hands MI tasks with ApEn feature (overlapping ratio~ 0.8) and SVM classifier. Hence the proposed method shows better results than several EEG MI classification methods proposed in the literature.