Towards Brain Decoding for Real-World Alertness Estimation

Abstract

A brain-computer interface (BCI) allows human to communicate with a computer by thoughts. Recent advances in brain decoding have shown the capability of BCIs in monitoring physiological and cognitive state of the brain, including alertness. Since drowsy driving has been an urgent issue in vehicle safety that causes numerous deaths and injuries, BCIs based on non-invasive electroencephalogram (EEG) are developed to monitor drivers’ alertness continuously and instantaneously. Nonetheless, on the pathway of transitioning laboratory-oriented BCI into real-world applications, there are major challenges that limit the usability and convenience for alertness estimation (AE). To completely understand the association between human EEG and alertness, this study employed a large-scale dataset collected from simulated driving experiments with a lane-keeping task and EEG recordings. An AE-BCI that acquires EEG from only non-hair-bearing (NHB) areas was proposed to maximize the comfort and convenience. The performance of the NHB AE-BCI was validated and compared with that using whole-scalp EEG, showing no significant difference in the accuracy of alert/non-alert classification. In addition, a subject-transfer framework that leverages large-scale existing data from other subjects was proposed to reduce the calibration time of an AE-BCI. Alert baseline data were involved to enhance the efficiency of subject-to-subject model transfer. The subject-transfer approach significantly reduced the calibration time of the AE-BCI, exhibiting the potential in facilitating plug-and-play brain decoding for real-world BCI applications. Overall, this study presents the contributions to developing an AE-BCI for real-world use with maximal usability and convenience. The methodologies and findings could further catalyze the exploration of real-world BCIs in more applications.