EEG-BASED TNN for Driver Vigilance Monitoring

Transformer neural network (TNN) has demonstrated its remarkable capacity to analyze and discern complex sequential datasets. This approach has achieved unprecedented success, particularly in the domain of natural language processing (NLP). TNN has since consistently proven to perform remarkably in other fields where long-term dependencies in the data are prevalent. Electroencephalography (EEG) data has historically posed a challenge for even modern deep neural networks to classify as EEG is notably complex and noisy, making training laborious and time-consuming. Though, there has been significant research done recently into the application of TNNs in EEG classification, often the task involved does not infer the TNN's ability for long-term dependencies. In this paper, we propose a TNN-based model for EEG-based driver vigilance monitoring, emphasizing the classification of driver vigilance states. This study utilized the data of 11 subjects taken from a public EEG dataset, focusing solely on single-channel analysis. Results indicate that the proposed TNN model can achieve average accuracies of up to 92.69% for Single-Subject analysis, 94.09% for Cross-Subject analysis and 74.74% for Leave-One-Subject-Out analysis, which surpasses state-of-the-art methods. The proposed TNN model's potential lies in not only driver vigilance state monitoring but also paving the way for broader applications of biosignal processing.