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Article

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

1
School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
2
Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, China
3
Industrial NeuroScience Lab, University of Rome “La Sapienza”, 00161 Rome, Italy
4
Department of Mathematics-Computer Science, Faculty of Science, Ain Shams University, Abbassia, Cairo 11435, Egypt
*
Author to whom correspondence should be addressed.
Academic Editor: Wai Lok Woo
Sensors 2021, 21(7), 2369; https://doi.org/10.3390/s21072369
Received: 11 March 2021 / Revised: 22 March 2021 / Accepted: 24 March 2021 / Published: 29 March 2021
(This article belongs to the Special Issue Embodied Minds: From Cognition to Artificial Intelligence)
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). View Full-Text
Keywords: cross-subject prediction; Domain-Adversarial Neural Network (DANN); electroencephalogram (EEG); Generative Adversarial Networks (GAN) cross-subject prediction; Domain-Adversarial Neural Network (DANN); electroencephalogram (EEG); Generative Adversarial Networks (GAN)
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MDPI and ACS Style

Zeng, H.; Li, X.; Borghini, G.; Zhao, Y.; Aricò, P.; Di Flumeri, G.; Sciaraffa, N.; Zakaria, W.; Kong, W.; Babiloni, F. An EEG-Based Transfer Learning Method for Cross-Subject Fatigue Mental State Prediction. Sensors 2021, 21, 2369. https://doi.org/10.3390/s21072369

AMA Style

Zeng H, Li X, Borghini G, Zhao Y, Aricò P, Di Flumeri G, Sciaraffa N, Zakaria W, Kong W, Babiloni F. An EEG-Based Transfer Learning Method for Cross-Subject Fatigue Mental State Prediction. Sensors. 2021; 21(7):2369. https://doi.org/10.3390/s21072369

Chicago/Turabian Style

Zeng, Hong, Xiufeng Li, Gianluca Borghini, Yue Zhao, Pietro Aricò, Gianluca Di Flumeri, Nicolina Sciaraffa, Wael Zakaria, Wanzeng Kong, and Fabio Babiloni. 2021. "An EEG-Based Transfer Learning Method for Cross-Subject Fatigue Mental State Prediction" Sensors 21, no. 7: 2369. https://doi.org/10.3390/s21072369

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