Abstract:
Driver drowsiness is a significant contributor to road accidents, leading to numerous
fatalities and hazards globally. Electroencephalography (EEG) has become a reliable
physiological signal for identifying drowsiness because it directly monitors brain activity.
However, creating a precise and calibration-free method for identifying driver drowsiness
is still difficult because EEG is affected by individual variations and changes in subjects.
In study proposed concise and easy-to-understand Long Short-Term Memory (LSTM)
model equipped with an attention mechanism to efficiently capture common EEG
characteristics among various individuals for detecting driver drowsiness. The attention
mechanism improved the model's interpretability by pinpointing crucial time steps in EEG
signals that have the greatest impact on classification. By conducting leave-one-subjectout (LOSO) cross-validation on 11 subjects, the model obtained an average accuracy of
79.1%, showcasing better results when compared to conventional techniques. Power
Spectral Density (PSD) analysis also showed that the model successfully captured
biologically significant characteristics, such as variations in Delta and Theta waves, that
are associated with drowsiness.
The proposed LSTM model with attention mechanism shows potential for practical use in
improving road safety through EEG-based drowsiness detection. Future research will
concentrate on enlarging the dataset, integrating multi-channel EEG data, and investigating
real-time application for practical utilization in the transportation sector.
