A mobile driver safety system: Analysis of single-channel EEG on drowsiness detection

Abstract

Recent studies reveal that driving without sufficient sleep would increase the risk of road traffic accident. With the aim to facilitate a safer driving experience, eye activity detection algorithm were studied actively. Though the use of wired multi-channel brain computer interface (BCI) to monitor driver's mental state has shown promising results, but the actual practicality were limited by its inconvenience. Consequently, we examined the effectiveness of a wireless and wearable single-channel BCI in detecting driver's eye-states. Using the NeuroSky MindWave headset that entailed a single-electrode for prefrontal cortex, we observed an increment of low alpha activity during the transition from eyes-open to eyes-closed state. A monitoring system to keep drivers awake by means of alarm notifications is then implemented using adaptive percentage threshold algorithm for alarm-triggering purpose. Through simulation, our algorithm has demonstrated an EEG eye-states recognition system with: adequate detection rate of 31% per second, negligible false alarm rate of 0.5%, and minimum latency of 2 seconds.