The characterization of complex patterns arising from electroencephalogram (EEG) is an important problem with significant applications in identifying different mental states. Based on the operational EEG of drivers, a method is proposed to characterize and distinguish different EEG patterns. The EEG measurements from seven professional taxi drivers were collected under different states. The phase characterization method was used to calculate the instantaneous phase from the EEG measurements. Then, the optimization of drivers’ EEG was realized through performing common spatial pattern analysis. The structures and scaling components of the brain networks from optimized EEG measurements are sensitive to the EEG patterns. The effectiveness of the method is demonstrated, and its applicability is articulated.
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Chen J, Wang H, Hua C. Electroencephalography based fatigue detection using a novel feature fusion and
extreme learning machine. Cognitive Systems Research. 2018;52(6): 715-728.
Fu R, Wang S, Wang S. Real-time Alarm Monitoring System for Detecting Driver Fatigue in Wireless Areas. Promet – Traffic&Transportation. 2017;29(2): 165-174.
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