An improved multi-source domain adaptation network for inter-subject mental fatigue detection based on DANN

• Published in: Biomedizinische Technik Vol. 68; no. 3; pp. 317 - 327
• Main Authors: Chen, Kun, Liu, Zhiyong, Li, Zhilei, Liu, Quan, Ai, Qingsong, Ma, Li
• Format: Journal Article
• Language: English
• Published: Germany De Gruyter 27.06.2023; Walter de Gruyter GmbH
• Subjects: Adaptation; Calibration; Classification; Datasets; domain adaptation; Domains; EEG; Electroencephalography; Fatigue; Feature extraction; FLDANN; inter-subject; mental fatigue; multi-source domain; Neural networks; Training; Transfer learning; FLDANN; mental fatigue; domain adaptation; inter-subject; EEG; multi-source domain
• DOI: 10.1515/bmt-2022-0354
• ISSN: 0013-5585; 1862-278X

Electroencephalogram (EEG) is often used to detect mental fatigue because of its real-time characteristic and objective nature. However, because of the individual variability of EEG among different individuals, tedious and time-consuming calibration sessions are needed. Therefore, we propose a multi-source domain adaptation network for inter-subject mental fatigue detection named FLDANN, which is short for focal loss based domain-adversarial training of neural network. As for mental state feature extraction, power spectrum density is extracted based on the Welch method from four sub-bands of EEG signals. The features of the source domain and target domain are fed into the FLDANN network. The contributions of FLDANN include: (1) It uses the idea of adversarial to reduce feature differences between the source and target domain. (2) A loss function named focal loss is used to assign weights to source and target domain samples. The experiment result shows that when the number of the source domains increases, the classification accuracy of domain-adversarial training of neural network (DANN) gradually decreases and finally tends to be stable. The proposed method achieves an accuracy of 84.10% ± 8.75% on the SEED-VIG dataset and 65.42% ± 7.47% on the self-designed dataset. In addition, the proposed method is compared with other domain adaptation methods and the results show that the proposed method outperforms those state-of-the-art methods. The result proves that the proposed method is able to solve the problem of individual differences across subjects and to solve the problem of low classification performance of multi-source domain transfer learning.