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4 CONCLUSIONS
The present work proposed a new hybrid method of
detecting drivers’ drowsiness based on time-
frequency analysis of EEG signals from a single
channel (FP1).
We extracted a total of eight features from the
three domains, the time, Fourier and PSD. After that,
we trained eight machine learning models, support
vector machine (with its four kernels), Gaussian
process (GP), K-Nearest-Neighbors (KNN), Multi-
layer Perceptron (MLP), and Decision Tree (DT).
We compared our work to previous ones based on
the same dataset available at the Physionet database,
and the use of a single channel of EEG records. The
added value of our model is the improvement of the
detection’s performance in the term of accuracy,
which achieved 95.7% and the processing time 0.062
seconds.
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Automatic Drowsiness Detection based on a Single Channel of EEG Signals using a Hybrid Analysis and Decision Tree Classification
Method under Python
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