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.
|Title of host publication||8th International Winter Conference on Brain-Computer Interface, BCI 2020|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 2020 Feb|
|Event||8th International Winter Conference on Brain-Computer Interface, BCI 2020 - Gangwon, Korea, Republic of|
Duration: 2020 Feb 26 → 2020 Feb 28
|Name||8th International Winter Conference on Brain-Computer Interface, BCI 2020|
|Conference||8th International Winter Conference on Brain-Computer Interface, BCI 2020|
|Country/Territory||Korea, Republic of|
|Period||20/2/26 → 20/2/28|
Bibliographical noteFunding 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).
© 2020 IEEE.
- Brain-Computer Interface
- Convolutional Neural Network
- Deep Learning
- Drowsiness Detection
ASJC Scopus subject areas
- Behavioral Neuroscience
- Cognitive Neuroscience
- Artificial Intelligence
- Human-Computer Interaction