Cognitive state identification based on Electroencephalography (EEG) not only helps to diagnose various types of cognitive dysfunctions as early as possible in the clinical field, but also enables timely detection of cognitive overload caused by subjects' participation in cognitive tasks, which could prevent many accidents from occurring. Deep learning (DL) plays a vital role in the recognition of cognitive states based on EEG. However, due to the significant individual differences of EEG, it remains a great challenge to design a robust deep learning model to identify the cognition state in time and effectively. In this study, a domain adaptive vision transformer framework DAEEGViT is proposed for cognitive state identification. In DAEEGViT, a MBConv module with a self-attention mechanism is introduced to extract the global and local features of EEG simultaneously, followed by a domain adaptation method to improve the transfer learning capability and a classifier to predict the cognitive state. Two self-collected datasets: FDD and ECED, with two public datasets: SEED and SEED-IV have been used in our experiments to identify the fatigue, cognitive impairment and emotion states. Compared with the normal ViT model, in the intra-subject experiments, the accuracy of DAEEGViT improved 25.28%, 54.70% and 43.70% on FDD, SEED, and SEED-IV, respectively. In the cross- subject experiments, it improved 1.23%, 1.85%, and 0.97% on three tasks of ECED (Happiness, neutral and sadness). On FDD, SEED, and SEED-IV, it improved 14.33%, 4.72% and 5.35%, respectively. The results prove that DAEEGViT has distinguished improvement in cognitive state recognition.
