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