An improved multi-source domain adaptation network for inter-subject mental fatigue detection based on DANN
- 1 February 2023
- journal article
- research article
- Published by Walter de Gruyter GmbH in Biomedizinische Technik/Biomedical Engineering
- Vol. 68 (3), 317-327
- https://doi.org/10.1515/bmt-2022-0354
Abstract
Objectives 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. Methods 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 Results 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. Conclusions 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.Keywords
Funding Information
- Natural Science Foundation of Hubei Province (2022CFB896)
- National Natural Science Foundation of China (52075398, 52275029)
This publication has 30 references indexed in Scilit:
- A multimodal approach to estimating vigilance using EEG and forehead EOGJournal of Neural Engineering, 2017
- Driver Drowsiness Estimation From EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR)IEEE Transactions on Fuzzy Systems, 2016
- Determination to the Confidence Level of PSD Estimation with Given D.O.F. Based on WELCH AlgorithmPublished by Atlantis Press SARL ,2015
- The Chalder Fatigue Scale (CFQ 11)Occupational Medicine, 2014
- A Transfer Learning Based Classifier Ensemble Model for Customer Credit ScoringPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- Domain Adaptation via Transfer Component AnalysisIEEE Transactions on Neural Networks, 2010
- A Survey on Transfer LearningIEEE Transactions on Knowledge and Data Engineering, 2009
- Central nervous system fatigue alters autonomic nerve activityLife Sciences, 2009
- Detecting Stress During Real-World Driving Tasks Using Physiological SensorsIEEE Transactions on Intelligent Transportation Systems, 2005
- The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodogramsIEEE Transactions on Audio and Electroacoustics, 1967