TY - GEN
T1 - EEG-based mental fatigue detection using cognitive tests and RVM classification
AU - Setiawan, Andi
AU - Wibawa, Adhi Dharma
AU - Pane, Evi Septiana
AU - Purnomo, Mauridhi Hery
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/3
Y1 - 2019/3
N2 - Mental fatigue (MF) is a common phenomenon in our daily lives. In the workplace, mental fatigue can increase the risk of human errors. Moreover, if it is not detected and handled correctly, it can cause various problems. Electroencephalogram (EEG) is one of many modalities that is widely used by researchers for detecting mental fatigue. However, due to the difficulty in provoking fatigue condition during the EEG measurement, this study proposed two experimental designs for detecting mental fatigue, the first design is by using physical induction (PI) and the 2nd design is by using mental induction (MI). We obtained the EEG signals from 20 healthy participants, from 14 channels of wireless EEG headset. Each participant was given four types of cognitive tests in the form of a computer game including Trail, Span, Stroop, and Arithmetic. The subjective measurement of fatigue condition was measured by using the Swedish Occupational Fatigue Inventory (SOFI) questionnaires. The EEG signal was extracted by using power percentage (PP) features of alpha (\alpha), beta (\beta), and theta (\theta) bands of frequency from all channels. As for classification, we introduced the use Relevance Vector Machine (RVM) approach which is claimed to have better performance than SVM as the precedence method. According to the results, all of the cognitive tests has increased average value of response time (RT) and decreased correction score (CS) on post-induction compared to pre-induction. Another result showed that the best mental fatigue detection is obtained from mental induction rather than physical induction. The classification results demonstrate an accuracy of 93.7% (MI) and 92.6% (PI) are achieved by RVM approach. Furthermore, RVM has also less execution time compared to SVM.
AB - Mental fatigue (MF) is a common phenomenon in our daily lives. In the workplace, mental fatigue can increase the risk of human errors. Moreover, if it is not detected and handled correctly, it can cause various problems. Electroencephalogram (EEG) is one of many modalities that is widely used by researchers for detecting mental fatigue. However, due to the difficulty in provoking fatigue condition during the EEG measurement, this study proposed two experimental designs for detecting mental fatigue, the first design is by using physical induction (PI) and the 2nd design is by using mental induction (MI). We obtained the EEG signals from 20 healthy participants, from 14 channels of wireless EEG headset. Each participant was given four types of cognitive tests in the form of a computer game including Trail, Span, Stroop, and Arithmetic. The subjective measurement of fatigue condition was measured by using the Swedish Occupational Fatigue Inventory (SOFI) questionnaires. The EEG signal was extracted by using power percentage (PP) features of alpha (\alpha), beta (\beta), and theta (\theta) bands of frequency from all channels. As for classification, we introduced the use Relevance Vector Machine (RVM) approach which is claimed to have better performance than SVM as the precedence method. According to the results, all of the cognitive tests has increased average value of response time (RT) and decreased correction score (CS) on post-induction compared to pre-induction. Another result showed that the best mental fatigue detection is obtained from mental induction rather than physical induction. The classification results demonstrate an accuracy of 93.7% (MI) and 92.6% (PI) are achieved by RVM approach. Furthermore, RVM has also less execution time compared to SVM.
KW - Cognitive tests
KW - EEG Signal
KW - Mental fatigue
KW - RVM Classification
UR - http://www.scopus.com/inward/record.url?scp=85073160279&partnerID=8YFLogxK
U2 - 10.1109/ICAIIT.2019.8834509
DO - 10.1109/ICAIIT.2019.8834509
M3 - Conference contribution
AN - SCOPUS:85073160279
T3 - Proceeding - 2019 International Conference of Artificial Intelligence and Information Technology, ICAIIT 2019
SP - 180
EP - 185
BT - Proceeding - 2019 International Conference of Artificial Intelligence and Information Technology, ICAIIT 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st International Conference of Artificial Intelligence and Information Technology, ICAIIT 2019
Y2 - 13 March 2019 through 15 March 2019
ER -