EEG-Based Driver Drowsiness Estimation Using Convolutional Neural Networks

Abstract

Deep learning, including convolutional neural networks (CNNs), has started finding applications in brain-computer interfaces (BCIs). However, so far most such approaches focused on BCI classification problems. This paper extends EEGNet, a 3-layer CNN model for BCI classification, to BCI regression, and also utilizes a novel spectral meta-learner for regression (SMLR) approach to aggregate multiple EEGNets for improved performance. Our model uses the power spectral density (PSD) of EEG signals as the input. Compared with raw EEG inputs, the PSD inputs can reduce the computational cost significantly, yet achieve much better regression performance. Experiments on driver drowsiness estimation from EEG signals demonstrate the outstanding performance of our approach.

Online Mental Fatigue Monitoring via Indirect Brain Dynamics Evaluation

“…line Applications. Some previous work derived from linear (Resalat & Saba, 2015;Lin et al, 2010) and nonlinear (Liu et al, 2016;Cui & Wu, 2017;Pan et al, 2020) methods show that it is possible to detect mental fatigue with high accuracy. It is impressive, but it would be rather blind to the wealth of the dynamics and behavioral variability (Müller et al, 2008;Ratcliff et al, 2009;Yarkoni et al, 2009;Wei et al, 2018;Cui et al, 2019).…”

“…In terms of data-driven mental fatigue evaluation, the reaction time (RT) to a certain assigned task is widely adopted as supervision, to indicate the fatigue level. Some linear (Lin et al, 2010;Resalat & Saba, 2015) and nonlinear (Liu, Lin, Wu, Chuang, & Lin, 2016;Cui & Wu, 2017;Pan, Tsang, Singh, Lin, & Sugiyama, 2020) methods show that it is possible to detect mental fatigue with high accuracy. It is impressive but also blind to the wealth of the dynamics and behavioral variability (Müller et al, 2008;Ratcliff, Philiastides, & Sajda, 2009;Yarkoni, Barch, Gray, Conturo, & Braver, 2009;Xu, Min, & Hu, 2018).…”

Investigating the P300 Response as a Marker of Working Memory in Virtual Training Environments

“…In many of these measurement-based systems the subject does not actively, explicitly, or voluntarily modulate EEG activity, instead focussing on the task [31]; this popular configuration is known as pBCI. In both real and virtual human-machine systems, PSD has been integrated to estimate levels of driver drowsiness [32], [33], in a pBCI configuration, allowing for interventions that improve safety and well-being of system users. This is accomplished with focus on the theta (4-7hz) and alpha (8-12hz) bands [34], [35], which studies have correlated with fatigue [36].…”

EEG-Based Driver Drowsiness Estimation Using Feature Weighted Episodic Training

“…The data were collected in a simulated driving experiment, which was identical to that used in [19], [21], [39], [40]. Sixteen healthy subjects (age 24.2 ± 3.7, ten males, six females) with normal or corrected to normal vision were recruited to participate in a sustained-attention driving experiment [39], [40], which consisted of a real vehicle mounted on a motion platform with six degrees of freedom immersed in a 360degree virtual reality scene.…”

“…Since EEG directly measures the brain states, it is very suitable for human psychophysiological state evaluation [18]. The power spectrum of EEG has been used to estimate driver drowsiness level [19]- [22], especially the theta (4-7Hz) and alpha (8-12Hz) bands [18], [23], [24]. Additionally, different brain regions have different abilities in assessing the driver's drowsiness level.…”