TY - GEN
T1 - VIGNet
T2 - 8th International Winter Conference on Brain-Computer Interface, BCI 2020
AU - Ko, Wonjun
AU - Oh, Kwanseok
AU - Jeon, Eunjin
AU - Suk, Heung Il
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (No. 2017-0-00451; Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning).
Publisher Copyright:
© 2020 IEEE.
PY - 2020/2
Y1 - 2020/2
N2 - Estimating driver fatigue is an important issue for traffic safety and user-centered brain-computer interface. In this paper, based on differential entropy (DE) extracted from electroencephalography (EEG) signals, we develop a novel deep convolutional neural network to detect driver drowsiness. By exploiting DE of EEG samples, the proposed network effectively extracts class-discriminative deep and hierarchical features. Then, a densely-connected layer is used for the final decision making to identify driver condition. To demonstrate the validity of our proposed method, we conduct classification and regression experiments using publicly available SEED-VIG dataset. Further, we also compare the proposed network to other competitive state-of-the-art methods with an appropriate statistical analysis. Furthermore, we inspect the real-world usability of our method by visualizing a change in the probability of driver status and confusion matrices.
AB - Estimating driver fatigue is an important issue for traffic safety and user-centered brain-computer interface. In this paper, based on differential entropy (DE) extracted from electroencephalography (EEG) signals, we develop a novel deep convolutional neural network to detect driver drowsiness. By exploiting DE of EEG samples, the proposed network effectively extracts class-discriminative deep and hierarchical features. Then, a densely-connected layer is used for the final decision making to identify driver condition. To demonstrate the validity of our proposed method, we conduct classification and regression experiments using publicly available SEED-VIG dataset. Further, we also compare the proposed network to other competitive state-of-the-art methods with an appropriate statistical analysis. Furthermore, we inspect the real-world usability of our method by visualizing a change in the probability of driver status and confusion matrices.
KW - Brain-Computer Interface
KW - Convolutional Neural Network
KW - Deep Learning
KW - Drowsiness Detection
KW - Electroencephalogram
UR - http://www.scopus.com/inward/record.url?scp=85084085676&partnerID=8YFLogxK
U2 - 10.1109/BCI48061.2020.9061668
DO - 10.1109/BCI48061.2020.9061668
M3 - Conference contribution
AN - SCOPUS:85084085676
T3 - 8th International Winter Conference on Brain-Computer Interface, BCI 2020
BT - 8th International Winter Conference on Brain-Computer Interface, BCI 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 26 February 2020 through 28 February 2020
ER -