Driver Drowsiness Immediately before Crashes – A Comparative Investigation of EEG Pattern Recognition

Periodogram and other spectral power estimation methods are established in quantitative EEG analysis. Their outcome in case of drowsy subjects fulfilling a sustained attention task is difficult to interpret. Two novel kind of EEG analyses based on pattern recognition were proposed recently, namely the micro-sleep (MS) and the alpha burst (AB) pattern recognition. The authors compare both methods by applying them to the same experimental data and relating their output variables to two independent variables of driver drowsiness. The latter was an objective lane tracking performance variable and the first was a subjective variable of self-experienced sleepiness. Results offer remarkable differences between both EEG analysis methodologies. The expected increase with time since sleep as well as with time on task, which also exhibited in both independent variables, was not identified after applying AB recognition. The EEG immediately before fatigue related crashes contained both patterns. MS patterns were remarkably more frequent before crashes; almost every crash (98.5 %) was preceded by MS patterns, where-as less than 64 % of all crashes had AB patterns within a 10 sec pre-crash interval.