Classification of the Imagination of the Left and Right Hand Movements using EEG

Abstract

Brain-computer interface (BCI) is a new and promising area of research which is assumed to help in solving a lot of problems especially for handicapped people. Detection of the imagination of the left and right hand movements can be used to control a wheelchair accordingly. Fortunately, modification of the brain activity caused by the imagination of the left or right hand movements is similar to the modification observed from a real left or right hand movements. The electrical activity of these modifications can be picked up from scalp electroencephalogram electrodes. In this work, we introduce a new method to detect and classify the imagination of the left and/or right hand movements. This method is based on exploring the time domain information in both alpha and beta rhythms using complex Morlet wavelet transform. Then, the fast Fourier transform is applied to explore the frequency domain information. The extracted features using both time and frequency domain information are then reduced using a feature subset selection algorithm. Then, the reduced features were fed into a multilayer backpropagation neural network to classify left from right hand movement imagination. The experimental results showed that the algorithm has reveals classification accuracy rates ranges from 97.77% to 100%, which are superior to the classification accuracy rates compared to other techniques.