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More than 25% of the fatal and injury car crashes are related to fatigue or drowsiness. This calls for the need for designing automated driver monitoring systems, which can continuously measure the drivers’ vigilance level and alert them if their cognitive state is not safe for driving anymore. One of the most reliable solutions is to directly measure the electrical activities of the brain to monitor the driver’s cognitive state. The proposed research aims to design a non-intrusive yet efficient monitoring system that uses electroencephalogram (EEG) signals. We propose to use new processing algorithms which allow us to reduce the number of EEG recording electrodes without deteriorating the system’s performance. The outcome of this research will be of great benefit to the industrial partner since it serves as a pilot project for developing cognition monitoring devices for users in more complicated situations such as surgeons, pilots, and industrial machine operators.
Dr. Konstantinos (Kostas) Plataniotis
Amirhossein Shokouh Aghaei
Qualcomm Canada Inc.
Engineering - computer / electrical
Information and communications technologies
University of Toronto
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