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Predicting a driver's cognitive state, or more specifically, modeling adriver's reaction time (RT) in response to the appearance of a potential hazardwarrants urgent research. In the last two decades, the electric field that isgenerated by the activities in the brain, monitored by an electroencephalogram(EEG), has been proven to be a robust physiological indicator of humanbehavior. However, mapping the human brain can be extremely challenging,especially owing to the variability in human beings over time, both within andamong individuals. Factors such as fatigue, inattention and stress can inducehomeostatic changes in the brain, which affect the observed relationshipbetween brain dynamics and behavioral performance, and thus make the existingsystems for predicting RT difficult to generalize. To solve this problem, anensemble-based weighted prediction system is presented herein. This systemcomprises a set of prediction submodels that are individually trained usinggroups of data with similar EEG-RT relationships. To obtain a final prediction,the prediction outcomes of the sub-models are then multiplied by weights thatare derived from the EEG alpha coherences of 10 channels plus theta band powersof 30 channels, whose changes were found to be indicators of variations in theEEG-RT relationship. The results thus obtained reveal that the proposed systemwith a time-varying adaptive weighting mechanism significantly outperforms theconventional system in modeling a driver's RT. The adaptive design of theproposed system demonstrates its feasibility in coping with the variability inthe brain-behavior relationship. In this contribution surprisingly simpleEEG-based adaptive methods are used in combination with an ensemble scheme tosignificantly increase system performance.

Dynamically Weighted Ensemble-based Prediction System for Adaptively Modeling Driver Reaction Time