TY - GEN
T1 - Classification of P300 in EEG signals for disable subjects using singular spectrum analysis
AU - Tjandrasa, Handayani
AU - Djanali, Supeno
AU - Arunanto, F. X.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - 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 of the classification gave the maximum accuracy of 92.5%. The average values of sensitivity, specificity, and accuracy were 70.8%, 89,8%, and 84.6% respectively.
AB - 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 of the classification gave the maximum accuracy of 92.5%. The average values of sensitivity, specificity, and accuracy were 70.8%, 89,8%, and 84.6% respectively.
KW - EEG signals
KW - P300
KW - event-related potential
KW - multilayer perceptron network
KW - singular spectrum analysis
UR - http://www.scopus.com/inward/record.url?scp=85047560679&partnerID=8YFLogxK
U2 - 10.1109/ICIIBMS.2017.8279747
DO - 10.1109/ICIIBMS.2017.8279747
M3 - Conference contribution
AN - SCOPUS:85047560679
T3 - ICIIBMS 2017 - 2nd International Conference on Intelligent Informatics and Biomedical Sciences
SP - 80
EP - 84
BT - ICIIBMS 2017 - 2nd International Conference on Intelligent Informatics and Biomedical Sciences
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Intelligent Informatics and Biomedical Sciences, ICIIBMS 2017
Y2 - 24 November 2017 through 26 November 2017
ER -