2018 : Classification of P300 in EEG signals for disable subjects using singular spectrum analysis

Prof.Ir. Supeno Djanali M.Sc Ph.D
Prof. Ir. Handayani Tjandrasa M.Sc., Ph.D.
Ir. F.X. Arunanto M.Sc.


Abstract

Brain-computer interfaces have been enabled severely disabled users to communicate with their environments. One method is to use a controlled stimulus to elicit the P300 event-related potential. EEG signals during the repeated stimuli were recorded from four disabled subjects and processed with a Butterworth bandpass filter and Singular Spectrum Analysis, normalized, separated into 2 groups of the target and non-target trial data, and averaged for every 5 trials for each group before classified using a neural network. The purpose of averaging every five target and non-target trials was to emerge the P300 component of even-related potentials so that the target trials could be differentiated from the non-target trials. Further processing by selecting 1 of every 5 processed non-target trials increased the value of sensitivity by 10.9%, it showed that the number of false negatives of target trials was reduced. The results …