Classification of left/right hand movement EEG signals using event related potentials and advanced features

Abstract

An event-related potentials (ERPs) is the measured brain response that is the direct result of a specific sensory, cognitive, or motor event. Brain-Computer Interface (BCI) is a device that enables the use of the brain’s neural activity to communicate with others or to control machines, artificial limbs, or robots without direct physical movements. In this paper, event-related potential (ERP) components of P300 and the advanced features combined an artificial neural network (ANN) were used to classify the electroencephalogram (EEG) signals associated with left and right hand movements. The EEG dataset used in this research was obtained from PhysioNet. Data was preprocessed using the EEGLAB MATLAB toolbox and then was epoched on the stimulation time for P300 and the basis of Event-Related (De) Synchronization (ERD/ERS) and movement-related cortical potentials (MRCP) features. Mu/beta rhythms were isolated for the ERD/ERS analysis and delta rhythms were isolated for the MRCP analysis. The final feature vector included the P300, ERD, ERS, and MRCP features in addition to the mean, power and energy of the activations of the epoched feature datasets. The datasets were inputted into ANN. The results of classification is quite good and it is promised to be used in a BCI context to mentally control a computer or machine.