Journal
2020 8TH INTERNATIONAL WINTER CONFERENCE ON BRAIN-COMPUTER INTERFACE (BCI)
Volume -, Issue -, Pages 34-36Publisher
IEEE
DOI: 10.1109/bci48061.2020.9061668
Keywords
Brain-Computer Interface; Electroencephalogram; Drowsiness Detection; Deep Learning; Convolutional Neural Network
Funding
- Institute of Information & Communications Technology Planning & Evaluation (IITP) - Korea government [2017-0-00451]
- Institute for Information & Communication Technology Planning & Evaluation (IITP), Republic of Korea [2017-0-00451-004] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available