DETECTION AND PREDICTION OF DRIVER’S MICROSLEEP EVENTS Martin Golz, David Sommer, Markus Holzbrecher, Thomas Schnupp University of Applied Sciences Schmalkalden D - 98 573 SCHMALKALDEN Germany Phone: +49 3683 688 4107 Fax: +49 3683 688 4499 E-mail: m.golz@fh-sm.de ABSTRACT The detection of spontaneous behavioral events like short episodes of unintentional sleep onset during driving, which are usually called microsleep events, still poses a challenge. The analysis of only a small number of signals seems to be useful to detect such events on a second-by-second basis. Here we present an experimental investigation of 22 young drivers in our real car driving simulation lab. The experimental design was chosen to raise many micro- sleep events. A framework for adaptive signal processing and subsequent discriminant analy- sis was applied. In addition to the common estimation of Power Spectral Densities, the recent- ly introduced method of Delay Vector Variance is utilized in order to get an estimate if the signal has undergone a modality change or not during the microsleep event under analysis. The fusion of the outcomes of both methods applied to three different types of signals, to the Electroencephalogram, the Electrooculogram and to Eyetracking signals, by modern methods of Computational Intelligence, namely the Support Vector Machine, leads to high classifica- tion accuracies with mean errors down to 9% for all subjects. It turned out that such low errors are only achievable in a relatively small temporal window around the onset of micro- sleep. Their prediction is feasible but with much higher errors. The signal processing frame- work has the potential to establish a reference standard for drowsiness and microsleep detection. 1 INTRODUCTION The detection of short-time brain states from ongoing biosignals is a challenging task not only in the area of clinical applications but also for e.g. future human-machine-interaction. As a special type of such an interface one can consider a system for detection of short intrusions of sleep into sustained wakefulness. In case of automobile drivers such events are believed to be a major factor in accident causation. During the recent years this topic has received broad attention from authorities, from the public and as well as from the research community. Most research projects in this area, e.g. the EU projects AWAKE (2001–2004) and SENSATION (2004–2007), are engaged in developing sensors to monitor driving impairments due to fati- gue and drowsiness. These impairments arise on a time scale of some ten seconds and are typically developing as waxing and waning patterns. Some doubts still exist about the feasibility of detecting short sleep intrusions under demands of attentiveness in ongoing biosignals on a time scale of, say, one to five seconds [Sagberg et al. 2004]. Many biosignals which are more or less coupled to drowsiness do not fulfill these temporal requirements. For example, electrodermal activity and galvanic skin resistance are too slow in their dynamics to detect such suddenly occurring events. The EEG is a relatively fast and direct functional reflection of mainly cortical and to some low degree also of subcortical acti- vities. Therefore, it should be the most promising signal for microsleep detection. The electro- oculogram (EOG) is a measurement of mainly eye and eyelid movements. Their endogenous