EEG-based microsleep detection using supervised learning
Tiredness and fatigue can often lead to brief instances of people falling asleep while engaged in some active task such as driving a motor vehicle. A study on fatigue by the General Association of German Insurance Industries, identified microsleep as the principal cause of 24% of fatal motorway accidents. Performance lapses range from brief pauses to “microsleeps”, which are brief, involuntary events of lapses in attention or responsiveness associated with events such as prolonged eye closure, blank stare etc. The aim of this project was to identify reliable physiological cues indicative of lapses, related to behavioural microsleep episodes, from the EEG, which could in turn be used to develop a real-time lapse detection (or better still, prediction) system.