Drowsiness Detection based on Continuous State Transitions from Alertness to Sleep using Forehead EEG

Drowsiness significantly contributes to accidents and decreased performance due to its impact on attention and concentration. Various sleepiness detection methods have been proposed but have not been widely adopted in daily life due to several limitations: (1) the lack of long-term measurement capability, (2) the need for external assistance for the operation, and (3) without the capability of reliable detection from drowsiness to sleep onset. In this pilot study, we investigated the characteristics of the forehead EEG from alertness sleep. The two-phase experiment was designed. In phase 1, subjects (N=13) had to sit in a chair and play a Sudoku puzzle, representing cognitive engagement in an altered state. In Phase 2, they lay on a bed and watched a first-person-perspective driving video record, which involved transitions from alertness to drowsiness and drowsiness to sleep. Validated with video data, forehead EEG detected an increase in eye-blink frequency, and the width of eye-blink peaks was also increased. After subjects fell asleep, forehead EEG exhibited alpha rhythms and slow eye movements, the mixing patterns observed in EEG and EOG. similar to standard EEG and EOG recordings. Moreover, our participants demonstrated a shorter sleep onset latency (1.23 ± 0.83 min) than previous sleep studies without fatigue induction. By integrating these characteristics with EEG- and EOG-based sleep score methods, forehead EEG presents a practical solution as a wearable device for daily drowsiness detection.