TY - GEN
T1 - Channel Selection of EEG-Based Cybersickness Recognition during Playing Video Game Using Correlation Feature Selection (CFS)
AU - Khoirunnisaa, Alfi Zuhriya
AU - Pane, Evi Septiana
AU - Wibawa, Adhi Dharma
AU - Purnomo, Mauridhi Hery
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/11/13
Y1 - 2018/11/13
N2 - Recently, the rapid development of 3D movie or video games, causing the phenomenon of cybersickness. Cybersickness is an unpleasant symptom (dizziness, nausea, vomiting, and disorientation) that occur to humans when exposure in 3D movie or video games within a certain time. It can disrupt psychic and physical condition of the human if not handled appropriately. Many studies have been done to investigate cybersickness using physiological measurements, one of which is EEG. However, earlier studies have not discussed an optimal channel location for identifying cybersickness on EEG. In this paper, we proposed Correlation Feature Selection (CFS) method to select features in order to determine best channel selection. The power percentage (PP) features of alpha (α), beta (β) and theta (θ) bands were extracted on all channels. CFS method obtained 3 optimal channels location on F3, O1, and O2 from PP feature of beta (β) band. The investigating of cybersickness employs three compare classifiers i.e. SVM-RBF, k-NN, and LDA. According to our result, LDA is the best classifier for identifying cybersickness. By using CFS method, it can improve performance accuracy from 83% to 100%. Hence, we conclude that beta frequency band on frontal and occipital area is suitable to measure EEG-based cybersickness.
AB - Recently, the rapid development of 3D movie or video games, causing the phenomenon of cybersickness. Cybersickness is an unpleasant symptom (dizziness, nausea, vomiting, and disorientation) that occur to humans when exposure in 3D movie or video games within a certain time. It can disrupt psychic and physical condition of the human if not handled appropriately. Many studies have been done to investigate cybersickness using physiological measurements, one of which is EEG. However, earlier studies have not discussed an optimal channel location for identifying cybersickness on EEG. In this paper, we proposed Correlation Feature Selection (CFS) method to select features in order to determine best channel selection. The power percentage (PP) features of alpha (α), beta (β) and theta (θ) bands were extracted on all channels. CFS method obtained 3 optimal channels location on F3, O1, and O2 from PP feature of beta (β) band. The investigating of cybersickness employs three compare classifiers i.e. SVM-RBF, k-NN, and LDA. According to our result, LDA is the best classifier for identifying cybersickness. By using CFS method, it can improve performance accuracy from 83% to 100%. Hence, we conclude that beta frequency band on frontal and occipital area is suitable to measure EEG-based cybersickness.
KW - CFS method
KW - EEG signal processing
KW - LDA
KW - SVM-RBF
KW - cybersickness
KW - k-NN classifier
UR - http://www.scopus.com/inward/record.url?scp=85058435496&partnerID=8YFLogxK
U2 - 10.1109/IBIOMED.2018.8534877
DO - 10.1109/IBIOMED.2018.8534877
M3 - Conference contribution
AN - SCOPUS:85058435496
T3 - Proceedings of 2018 2nd International Conference on Biomedical Engineering: Smart Technology for Better Society, IBIOMED 2018
SP - 48
EP - 53
BT - Proceedings of 2018 2nd International Conference on Biomedical Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Biomedical Engineering, IBIOMED 2018
Y2 - 24 July 2018 through 26 July 2018
ER -