An Electroencephalography (EEG) Dataset for Understanding Driving Expertise from Naturalistic Urban Road Experiments

This dataset captures comprehensive multi-modal recordings from 10 expert and 10 novice drivers navigating 13 naturalistic urban driving conditions, synchronizing EEG brain activity with vehicle CAN bus data, traffic information, and psychophysiological measures (EDA and HR). Uniquely, it includes physiological responses and subjective feedback from two passengers per trip to validate driving performance quality, along with pre/post-experiment questionnaires and semi-structured interviews from all participants. The dataset enables researchers to decode neural signatures of driving expertise and understand the cognitive mechanisms underlying expert decision-making in complex urban environments. This resource provides crucial insights for developing more human-like autonomous driving algorithms by bridging the gap between human driving expertise and artificial intelligence. The multi-perspective approach combining driver neural data with passenger validation offers an unprecedented opportunity to understand what makes expert drivers excel in safety, comfort, and intelligent navigation.