EEG classification by Autocorrelation-Pulse in left and right motor imaginary data

This paper proposes a classification method for imaginary right and left motor EEG using a new algorithm named Autocorrelation-Pulse (AP). This algorithm is based on the spatiotemporal pulse patterns generated from the autocorrelation values in the ongoing EEG data. A backpropagation feedforward neural network was used for classification. The structure of the network preserves the spatio-temporal characteristics of the signal. Simulation results show that the classification accuracy can reach 100% on each subject and 91% over all subjects when the correct pair of electrodes is selected.