Drowsiness Detection via EEG/ECG
Feature trajectories from EEG/ECG markers of fatigue.Summary
To keep operators safe, we studied physiological correlates of fatigue using four EEG bands—delta, theta, alpha, beta—paired with ECG-derived metrics. The combined feature space captures transitions into drowsy states earlier than behavioral cues, enabling proactive alerts.
Outcomes
- Generated labeled datasets for fatigue modeling in vehicles and control rooms
- Validated that multimodal biosignals outperform single-modality detectors
- Provided groundwork for subsequent remote cognitive assessment tools
Description
Studies have investigated various physiological associations with fatigue to try to identify fatigue indicators. The current study assessed the four electroencephalography (EEG) activities, delta (d), theta (h), alpha (a) and beta (b).
Cheolsoo Park
Professor
His research interests include machine learning, adaptive signal processing, computational neuroscience, and wearable technology.