Electroencephalogram-Based Approaches for Driver Drowsiness Detection and Management: A Review

Abstract

Drowsiness is not only a core challenge to safe driving in traditional driving conditions but also a serious obstacle for the wide acceptance of added services of self-driving cars (because drowsiness is, in fact, one of the most representative early-stage symptoms of self-driving carsickness). In view of the importance of detecting drivers' drowsiness, this paper reviews the algorithms of electroencephalogram (EEG)-based drivers' drowsiness detection (DDD). To facilitate the review, the EEG-based DDD approaches are organized into a tree structure taxonomy, having two main categories, namely "detection only (open-loop)" and "management (closed-loop)", both aimed at designing better DDD systems that ensure early detection, reliability and practical utility. To achieve this goal, we addressed seven questions, the answers of which helped in developing an EEG-based DDD system that is superior to the existing ones. A basic assumption in this review article is that although driver drowsiness and carsickness-induced drowsiness are caused by different factors, the brain network that regulates drowsiness is the same.

Keywords: EEG; brain stimulation; closed-loop algorithms; drivers’ drowsiness detection; machine learning.

Figure 1

Block diagram of a typical EEG-based DDD system.

Figure 2

Hierarchical taxonomy for EEG-based DDD algorithms dealing with seven primary research questions and corresponding selection criteria for these questions.

Figure 3

The widely used EEG electrode locations. Totally, 81 EEG channel locations are presented, in which the red circles refer to commonly used occipital region.

Figure 4

The type of EEG channels. Bipolar type (top) and unipolar type or monopolar (bottom).