TY - GEN
T1 - A novel EEG-based detection method of emergency situations for assistive vehicles
AU - Teng, Teng
AU - Bi, Luzheng
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/5/11
Y1 - 2017/5/11
N2 - This paper presents a new Electroencephalography (EEG)-based method to detect emergency situations while drivers employ a brain-machine interface but not using limbs to operate an assistive vehicle. EEG signals were first preprocessed to remove the blinking artifact. The sums of powers of five rhythms (including alpha, delta, beta, theta, and low gamma rhythms) from 16 channels were then computed as the original feature pool. After that, Chi-square feature extraction method was employed to select features as the input of the Fisher linear classifier. The experimental results indicate that the proposed model can issue a braking command 400ms earlier than drivers with the system accuracy of 91.72% on average. The new detection model can be used to help develop a complementary driver assistant system to existing ones to improve the safety of brain-controlled driving and even driving with limbs.
AB - This paper presents a new Electroencephalography (EEG)-based method to detect emergency situations while drivers employ a brain-machine interface but not using limbs to operate an assistive vehicle. EEG signals were first preprocessed to remove the blinking artifact. The sums of powers of five rhythms (including alpha, delta, beta, theta, and low gamma rhythms) from 16 channels were then computed as the original feature pool. After that, Chi-square feature extraction method was employed to select features as the input of the Fisher linear classifier. The experimental results indicate that the proposed model can issue a braking command 400ms earlier than drivers with the system accuracy of 91.72% on average. The new detection model can be used to help develop a complementary driver assistant system to existing ones to improve the safety of brain-controlled driving and even driving with limbs.
KW - EEG
KW - brain-controlled vehicles
KW - driving safety
KW - emergency situations
KW - human-machine interface
UR - http://www.scopus.com/inward/record.url?scp=85020195744&partnerID=8YFLogxK
U2 - 10.1109/ICIST.2017.7926780
DO - 10.1109/ICIST.2017.7926780
M3 - Conference contribution
AN - SCOPUS:85020195744
T3 - 7th International Conference on Information Science and Technology, ICIST 2017 - Proceedings
SP - 335
EP - 339
BT - 7th International Conference on Information Science and Technology, ICIST 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Information Science and Technology, ICIST 2017
Y2 - 16 April 2017 through 19 April 2017
ER -