| Sumario: | Abstract: Driver fatigue is a major cause of traffic accidents.
Electroencephalogram (EEG) is considered one of the most
reliable predictors of fatigue. This paper proposes a novel,
simple and fast method for driver fatigue detection that can be
implemented in real-time by using a single-channel on the scalp.
The study has two objectives. The first consists of determining
the single most relevant EEG channel to monitor fatigue. This is
done using maximum covariance analysis. The second objective
consists in developing a deep learning method to detect fatigue
from this single channel. For this purpose, spectral features of
the signal are first extracted. The sequence of features is used to
train a Long Short Term Memory (LSTM), deep learning model,
to detect fatigue states. Experiments with 12 EEG signals were
conducted to discriminate the fatigue stage from the alert stage.
Results showed that TP7 was the most significant channel, which
is located in the left tempo-parietal region. A zone associated with
spatial awareness, visual-spatial navigation, and the cautiousness
faculty. In addition, despite the small dataset, the proposed
method predicts fatigue with 75% accuracy and a 1.4-second
delay. These promising results provide new insights into relevant
data for monitoring driver fatigue.
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