  Drowsiness state of a driver is a topic of extensive discussion due to its
significant role in causing traffic accidents. This research presents a novel
approach that combines Fuzzy Common Spatial Patterns (CSP) optimised Phase
Cohesive Sequence (PCS) representations and fuzzy CSP-optimized signal
amplitude representations. The research aims to examine alterations in
Electroencephalogram (EEG) synchronisation between a state of alertness and
drowsiness, forecast drivers' reaction times by analysing EEG data, and
subsequently identify the presence of drowsiness. The study's findings indicate
that this approach successfully distinguishes between alert and drowsy mental
states. By employing a Deep Autoencoder-based data fusion technique and a
regression model such as Support Vector Regression (SVR) or Least Absolute
Shrinkage and Selection Operator (LASSO), the proposed method outperforms using
individual feature sets in combination with a regressor model. This superiority
is measured by evaluating the Root Mean Squared Error (RMSE), Mean Absolute
Percentage Error (MAPE), and Correlation Coefficient (CC). In other words, the
fusion of autoencoder-based amplitude EEG power features and PCS features, when
used in regression, outperforms using either of these features alone in a
regressor model. Specifically, the proposed data fusion method achieves a
14.36% reduction in RMSE, a 25.12% reduction in MAPE, and a 10.12% increase in
CC compared to the baseline model using only individual amplitude EEG power
features and regression.
