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|Title: ||Real-time assessment of vigilance level using an innovative Mindo4 wireless EEG system|
|Authors: ||Chin-Teng Lin|
NTOU:Department of Computer Science and Engineering
|Keywords: ||Electroencephalography;Fatigue;Wireless communication;Vehicles;Spectral analysis;Wireless sensor networks;Forehead|
|Issue Date: ||2018-05-21T03:42:00Z
|Publisher: ||Circuits and Systems (ISCAS), 2013 IEEE International Symposium on|
Monitoring the neurophysiological activities of driver in an operational environment poses a severe measurement challenge using a current laboratory-oriented biosensor technology. The aims of this research are to 1) introduce a dry and wireless EEG system used for conveniently recording EEG signals from forehead regions, 2) propose an effective system for processing EEG recordings and translating them into the vigilance level, and 3) implement the proposed system with a JAVA-based graphical user interface (GUI) for online analysis. To validate the performance of the proposed system, this study recruited eight voluntary subjects to participate a 90-min sustained-attention driving task in a virtual-realistic driving environment. Physiological evidence obtained from the power spectral analysis showed that the dry EEG system could distinguish an alert EEG from a drowsy EEG by evaluating the spectral dynamics of delta and alpha activities. Furthermore, the experimental result of the comparison of the prediction performance using four forehead electrode sites (AF8, FP2, FP1, and AF7) implied that a single-electrode EEG signal used in the mobile and wireless EEG system is able to obtain a high prediction accuracy (~93%). Taken together, the proposed system applied a dry-EEG device combined with an effective algorithm can be a promising technology for real driving applications.
|Appears in Collections:||[資訊工程學系] 演講及研討會|
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