Detecting driver drowsiness based on single electroencephalography channel

In the recent years, driver drowsiness has been considered one of the major causes of road accidents, which can lead to severe physical injuries, deaths and important economic losses. As a consequence, a reliable driver drowsiness-detection-system is necessary to alert the driver before an accident happens. For this reason, an Electroencephalogram (EEG) has recently drawn attention in the field of brain-computer interface and cognitive neuroscience to control and predict the human drowsiness state. Our objective in this work, is to proposed an automatic approach to detect the occurrence of driver drowsiness onset based on the Artificial Neuronal Network (ANN) and using only one EEG channel. In this study, an experiment has been conducted on ten human subjects using nine features computed from one EEG channel using the Fast Fourier Transform(FFT). After introducing these features in an ANN classifier, we have obtained a classification accuracy rate of 86.1% and 84.3% of drowsiness and alertness detection. All features used in this work are easy to calculate and can be determined in real time, which makes this approach adapted for embedded implementation.