The impact of secondary tasks on drivers during naturalistic driving: Analysis of EEG dynamics

Driver distraction is a significant cause of accidents leading to injuries and fatalities on the roadway. Driving is a complex task and demands continuous visual and cognitive attention on the primary task of driving. Drivers are at risk of responding more slowly or less suitable to intricate and dynamically changing situations that entail their complete attention. Therefore, monitoring and detection of driver distraction can directly facilitate decreasing the costs associated with roadway disasters. There has been much research effort toward developing automatic detectors of drivers' distraction state. Many approaches were based on analyzing the driving behavior or using camera-based techniques to monitor and evaluate driver distraction. However, besides the issues with privacy and consumer adoption, there are limitations with how early they can detect the signs of distraction. Neurophysiological signals such as Electroencephalogram (EEG) and brain activity analysis have been extensively used to understand the precursors of distraction at the physiological level to develop systems to alert the drivers well in advance. The objective of this study is to take a step toward establishing a systematic framework to extract effective descriptors and to measure the impact of in-vehicle secondary tasks on driver cognitive state during naturalistic driving by capturing the changes in EEG dynamics. We employed our wearable data acquisition platform to collect wireless EEG data from six subjects during a naturalistic driving session and investigated six potentially distracting stimuli. We present a standard analysis framework to examine the impact of various EEG signal pre-processing, feature extraction, and classification methods in order to detect driver engagement in a secondary task with high accuracy (98.99 ± 1.2%) solely based on their recorded EEG.