An EEG-Based Transfer Learning Method for Cross-Subject Fatigue Mental State Prediction

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 7; p. 2369
• Main Authors: Zeng, Hong, Li, Xiufeng, Borghini, Gianluca, Zhao, Yue, Aricò, Pietro, Di Flumeri, Gianluca, Sciaraffa, Nicolina, Zakaria, Wael, Kong, Wanzeng, Babiloni, Fabio
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 29.03.2021; MDPI
• Subjects: Acoustics; Algorithms; Brain-Computer Interfaces; cross-subject prediction; Domain-Adversarial Neural Network (DANN); electroencephalogram (EEG); Electroencephalography; Experiments; Fatigue; Generative adversarial networks; Generative Adversarial Networks (GAN); Humans; Machine Learning; Methods; Neural networks; Neural Networks, Computer; Physiology; Questionnaires; Traffic accidents; Workloads; Domain-Adversarial Neural Network (DANN); Generative Adversarial Networks (GAN); cross-subject prediction; electroencephalogram (EEG)
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21072369

Fatigued driving is one of the main causes of traffic accidents. The electroencephalogram (EEG)-based mental state analysis method is an effective and objective way of detecting fatigue. However, as EEG shows significant differences across subjects, effectively "transfering" the EEG analysis model of the existing subjects to the EEG signals of other subjects is still a challenge. Domain-Adversarial Neural Network (DANN) has excellent performance in transfer learning, especially in the fields of document analysis and image recognition, but has not been applied directly in EEG-based cross-subject fatigue detection. In this paper, we present a DANN-based model, Generative-DANN (GDANN), which combines Generative Adversarial Networks (GAN) to enhance the ability by addressing the issue of different distribution of EEG across subjects. The comparative results show that in the analysis of cross-subject tasks, GDANN has a higher average accuracy of 91.63% in fatigue detection across subjects than those of traditional classification models, which is expected to have much broader application prospects in practical brain-computer interaction (BCI).