Abstract
Deep learning (DL) has been demonstrated to be a valuable tool for classifying state of disorders of consciousness (DOC) using EEG signals. However, the performance of the DL-based DOC state classification is often challenged by the limited size of EEG datasets. To overcome this issue, we introduce multiple open-source EEG datasets to increase data volume and train a novel multi-task pre-training Transformer model named MutaPT. Furthermore, we propose a cross-distribution self-supervised (CDS) pre-training strategy to enhance the model's generalization ability, addressing data distribution shifts across multiple datasets. An EEG dataset of DOC patients is used to validate the effectiveness of our methods for the task of classifying DOC states. Experimental results show the superiority of our MutaPT over several DL models for EEG classification.