Abstract
Distraction while driving is a serious problem that can have many catastrophic consequences. Developing a countermeasure to detect the drivers’ distraction is imperative. This study measured Electroencephalography (EEG) signals from six healthy participants while they were asked to pay their full attention to a lane-keeping driving task or a math problem-solving task. The time courses of six distinct brain networks (Frontal, Central, Parietal, Occipital, Left Motor, and Right Motor) separated by Independent Component Analysis were used to build the distraction-detection model. EEG data were segmented into 400-ms epochs. Across subjects, 80% of the EEG epochs were used to train various classifiers that were tested against the remaining 20% of the data. The classification performance based on support vector machines (SVM) with a radial basis function (RBF) kernel achieved accuracy of 84.7±2.7% or 85.8±1.3% for detecting subjects’ focuses of attention to the math-solving or lane-deviation task, respectively. The high attention-detection accuracy demonstrated the feasibility of accurately detecting drivers’ attention based on the brain activities. This demonstration may lead to a practical real-time distraction-detection system for improving road safety.
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Wang, YK., Jung, TP., Chen, SA., Huang, CS., Lin, CT. (2013). Tracking Attention Based on EEG Spectrum. In: Stephanidis, C. (eds) HCI International 2013 - Posters’ Extended Abstracts. HCI 2013. Communications in Computer and Information Science, vol 373. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39473-7_90
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DOI: https://doi.org/10.1007/978-3-642-39473-7_90
Publisher Name: Springer, Berlin, Heidelberg
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