Title:
Towards Real-world Alertness Estimation using Brain-Computer Interfacing
Towards Real-world Alertness Estimation using Brain-Computer Interfacing
Time:
07/28 (Sat.) 7 pm PDT, 8 pm MDT, 9 pm CDT, 10 pm EDT
07/29 (Sun.) 10 am Taiwan
07/28 (Sat.) 7 pm PDT, 8 pm MDT, 9 pm CDT, 10 pm EDT
07/29 (Sun.) 10 am Taiwan
Keywords:
Neuroengineering, Brain-computer interface, Alertness estimation, EEG signal processing, Machine learning, Cognitive sensing
Neuroengineering, Brain-computer interface, Alertness estimation, EEG signal processing, Machine learning, Cognitive sensing
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’ drowsiness 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 monitoring. To completely understand the association between human EEG and alertness level, this study employed a large-scale dataset collected from simulated driving experiments with a lane-keeping task and EEG recordings. Overall, this study presents the contributions to developing a BCI for real-world alertness monitoring with maximal usability and convenience. The methodologies and findings could further catalyze the exploration of real-world BCIs in more applications.
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’ drowsiness 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 monitoring. To completely understand the association between human EEG and alertness level, this study employed a large-scale dataset collected from simulated driving experiments with a lane-keeping task and EEG recordings. Overall, this study presents the contributions to developing a BCI for real-world alertness monitoring with maximal usability and convenience. The methodologies and findings could further catalyze the exploration of real-world BCIs in more applications.
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