TY - GEN
T1 - Investigating Window Segmentation on Mental Fatigue Detection Using Single-Channel EEG
AU - Hendrawan, Muhammad Afif
AU - Pane, Evi Septiana
AU - Wibawa, Adhi Dharma
AU - Purnormo, Mauridhi Hery
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/11/15
Y1 - 2018/11/15
N2 - Mental fatigue condition can be a serious problem if it is not handled properly. It also has a correlation with acute or chronic illness. Many research has been done to detect mental fatigue condition using several methods. The Physiological method is proved as a robust indicator, one of which is electroencephalogram (EEG). EEG is the most widely used as a physiological indicator in the few decades. However, most of the research in mental fatigue detection based on EEG used long time segment and complex computation method. In this paper, a window segmentation was employed to investigate mental fatigue information that might contain in a specific segment. Power percentage feature was extracted from each segment. The detection of mental fatigue employs three classifiers, LDA, QDA, and SVM. According to our experiment, LDA yields the highest performance with 92.82 % of accuracy. This result obtained from 30s length window segment which contains only the first and the last segment of the EEG signal data points. This result showed that information of mental fatigue in EEG signal may be better detected in short time segment and can be found in specific window segment.
AB - Mental fatigue condition can be a serious problem if it is not handled properly. It also has a correlation with acute or chronic illness. Many research has been done to detect mental fatigue condition using several methods. The Physiological method is proved as a robust indicator, one of which is electroencephalogram (EEG). EEG is the most widely used as a physiological indicator in the few decades. However, most of the research in mental fatigue detection based on EEG used long time segment and complex computation method. In this paper, a window segmentation was employed to investigate mental fatigue information that might contain in a specific segment. Power percentage feature was extracted from each segment. The detection of mental fatigue employs three classifiers, LDA, QDA, and SVM. According to our experiment, LDA yields the highest performance with 92.82 % of accuracy. This result obtained from 30s length window segment which contains only the first and the last segment of the EEG signal data points. This result showed that information of mental fatigue in EEG signal may be better detected in short time segment and can be found in specific window segment.
KW - EEG signal
KW - brain computer interface
KW - mental fatigue
UR - http://www.scopus.com/inward/record.url?scp=85059401855&partnerID=8YFLogxK
U2 - 10.1109/ICICI-BME.2017.8537716
DO - 10.1109/ICICI-BME.2017.8537716
M3 - Conference contribution
AN - SCOPUS:85059401855
T3 - Proceedings of 2017 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, ICICI-BME 2017
SP - 173
EP - 178
BT - Proceedings of 2017 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, ICICI-BME 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, ICICI-BME 2017
Y2 - 6 November 2017 through 7 November 2017
ER -