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; Generative Adversarial Networks (GAN); Humans; Machine Learning; Methods; Neural networks; Neural Networks, Computer; Physiology; Questionnaires; Workloads; Domain-Adversarial Neural Network (DANN); Generative Adversarial Networks (GAN); cross-subject prediction; electroencephalogram (EEG)

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 o...