Temporal Impact on Cognitive Distraction Detection for Car Drivers using EEG

Electroencephalography (EEG) has the potential to measure a person’s cognitive state, however, we still only have limited knowledge about how well-suited EEG is for recognising cognitive distraction while driving. In this paper, we present DeCiDED, a system that uses EEG in combination with machine learning to detect cognitive distraction in car drivers. Through DeCiDED, we investigate the temporal impact, of the time between the collection of training and evaluation data, and the detection accuracy for cognitive distraction. Our results indicate, that DeCiDED can recognise cognitive distraction with high accuracy when training and evaluation data are originating from the same driving session. Further, we identify a temporal impact, resulting in reduced classification accuracy, of an increased time-span between different drives on the detection accuracy. Finally, we discuss our findings on cognitive attention recognition using EEG how to complement it to categorise different types of distractions.