Motor Imagery EEG Classification Using Random Subspace Ensemble Network with Variable Length Features

• Published in: Bioautomation Vol. 25; no. 1; pp. 13 - 24
• Main Authors: Al Nazi, Zabir, Hossain, A. B. M. Aowlad, Islam, Md. Monirul
• Format: Journal Article
• Language: English
• Published: Sophia Bulgarska Akademiya na Naukite / Bulgarian Academy of Sciences 01.03.2021; Bulgarian Academy of Sciences
• Subjects: Accuracy; brain computer interface; Classification; discrete wavelet transform; dual tree complex wavelet transform; Electroencephalography; random subspace ensemble network; Wavelet transforms

Classification of electroencephalography (EEG) signals for brain-computer interface has great impact on people having various kinds of physical disabilities. Motor imagery EEG signals of hand and leg movement classification can help people whose limbs are replaced by prosthetics. In this paper, rand...