Drowsy driving causes severe road traffic accidents and significantly threatens road driving. Recently, electroencephalogram (EEG)-based drowsiness state classification has gained attention in the field of brain-computer interface (BCI). Because of the inter-and intra-subject variability of EEG signals, EEG-based drowsiness state classification is still challenging in developing an estimator applicable to unseen subjects. Generally, calibration sessions are required to tune the model with subject-specific data. In this paper, we propose an EEG-based driver drowsiness state (i.e., alert and drowsy) classification framework that improves the generalization performance to unseen subjects. Style features of multi-domain instances are mixed to generate unseen domains, and the distance of labels within classes is minimized to learn robust representations. Experiments were conducted on EEG data acquired from a drowsy driving experiment in a simulated-driving environment. Our proposed framework achieved an accuracy of 77.26%, an F1-score of 0.6266, and a recall of 0.6813 across eleven subjects in leave-one-subject-out cross-validation. The experimental results showed an improvement in the generalization performance for novel target subjects in driver drowsiness state classification and demonstrated the potential for calibration-free BCI.
|Title of host publication||2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||Published - 2022|
|Event||2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022 - Prague, Czech Republic|
Duration: 2022 Oct 9 → 2022 Oct 12
|Name||Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics|
|Conference||2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022|
|Period||22/10/9 → 22/10/12|
Bibliographical noteFunding Information:
This work was partly supported by Institute of Information communications Technology Planning Evaluation (IITP) grants funded by the Korea government (MSIT) (No. 2017-0-00451: Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning, No. 2019-0-00079: Artificial Intelligence Graduate School Program, Korea University, No. 2021-0-02068: Artificial Intelligence Innovation Hub, and No. 2021-0-00866: Development of BMI application technology based on multiple bio-signals for autonomous vehicle drivers).
© 2022 IEEE.
- Brain-computer interface
- Domain generalization
- Driver drowsiness state classification
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Control and Systems Engineering
- Human-Computer Interaction