HATNet: EEG-Based Hybrid Attention Transfer Learning Network for Train Driver State Detection

Abstract

Electroencephalography (EEG) is widely utilized for train driver state detection due to its high accuracy and low latency. However, existing methods for driver status detection rarely use the rich physiological information in EEG to improve detection performance. Moreover, there is currently a lack of EEG datasets for abnormal states of train drivers. To address these gaps, we propose a novel transfer learning model based on a hybrid attention mechanism, named hybrid attention-based transfer learning network (HATNet). We first segment the EEG signals into patches and utilize the hybrid attention module to capture local and global temporal patterns. Then, a channel-wise attention module is introduced to establish spatial representations among EEG channels. Finally, during the training process, we employ a calibration-based transfer learning strategy, which allows for adaptation to the EEG data distribution of new subjects using minimal data. To validate the effectiveness of our proposed model, we conduct a multistimulus oddball experiment to establish a EEG dataset of abnormal states for train drivers. Experimental results on this dataset indicate that: 1) Compared to the state-of-the-art end-to-end models, HATNet achieves the highest classification accuracy in both subject-dependent and subject-independent tasks at 94.26% and 87.03%, respectively, and 2) The proposed hybrid attention module effectively captures the temporal semantic information of EEG data.