Detection of multi-class emergency situations during simulated driving from ERP

We present a driving simulator study investigating whether a driver's braking intention in emergency situations can be detected under more general circumstances than previously described in the literature. Precisely, we here simulated three kinds of realistic emergency situations instead of only one as considered in Haufe et al., 2011. For each of the three situations, the analysis of electroencephalography (EEG) data reveals a different characteristic spatio-temporal event-related potential (ERP) sequence. For all stimuli, topographical maps of area under the curve (AUC) scores related to the discrimination between emergency and normal driving situations show a significant positive deflection in parietal regions about 300ms post-stimulus. Thus, it is possible to predict different emergency situations from EEG before the actual braking. A classification analysis indeed reveals that EEG-based emergency braking detection can be performance faster than electromyography- or pedal-based detection, while being as robust.