EEG-Based Attention Tracking During Distracted Driving

Distracted driving might lead to many catastrophic consequences. Developing a countermeasure to track drivers' focus of attention (FOA) and engagement of operators in dual (multi)-tasking conditions is thus imperative. Ten healthy volunteers participated in a dual-task experiment that comprised two tasks: a lane-keeping driving task and a mathematical problem-solving task (e.g., 24+15=37?) during which their electroencephalogram (EEG) and behaviors were concurrently recorded. Independent component analysis (ICA) was employed as a spatial filter to separate the contributions of independent sources from the recorded EEG data. The power spectra of six components (i.e., frontal, central, parietal, occipital, left motor, and right motor) extracted from single-task conditions were fed into support vector machine (SVM) based on the radial basis function (RBF) kernel to build an FOA assessment system. The system achieved 84.6±5.8% and 86.2±5.4% classification accuracies in detecting the participants' FOAs on the math versus driving tasks, respectively. This FOA assessment system was then applied to evaluate participants' FOAs during dual-task conditions. The detected FOAs revealed that participants' cognitive attention and strategies dynamically changed between tasks to optimize the overall performance, as attention was limited and competed. The empirical results of this study demonstrate the feasibility of a practical system to continuously estimating cognitive attention through EEG spectra.