Classification of 2D hand movement with power spectrum estimation

Abstract

Brain-Computer Interfacing (BCI) translates real and imaginary movements of body parts to communicate with computers. Estimation of physical movements based on Electroencephalography (EEG) signals is the prime motive behind the classification of different tasks and movements which navigate the BCI system. Hand movements categorization in earlier studies of BCI provided only two classes. In our new approach, the further classification of hand movements in four classes is performed. Backward and forward movements of right and left hands are estimated by using computational tools like Independent Component Analysis (ICA) and power spectrum analysis.