Recognizing drivers' sleep onset by detecting slow eye movement using a parallel multimodal one-dimensional convolutional neural network.

Abstract

Slow eye movements (SEMs) are a reliable physiological marker of drivers' sleep onset, often accompanied by EEG alpha wave attenuation. A parallel multimodal 1D convolutional neural network (PM-1D-CNN) model is proposed to classify SEMs. The model uses two parallel 1D-CNN blocks to extract features from EOG and EEG signals, which are then fused and fed into fully connected layers for classification. Results show that the PM-1D-CNN outperforms the SGL-1D-CNN and Bimodal-LSTM networks in both subject-to-subject and cross-subject evaluations, confirming its effectiveness in detecting sleep onset.