An improved multi-source domain adaptation network for inter-subject mental fatigue detection based on DANN
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Kun Chen
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Zhiyong Liu
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.
Funding source: Natural Science Foundation of Hubei Province
Award Identifier / Grant number: 2022CFB896
Funding source: National Natural Science Foundation of China
Award Identifier / Grant number: 52075398
Award Identifier / Grant number: 52275029
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Research funding: National Natural Science Foundation of China (52275029, 52075398) and Natural Science Foundation of Hubei Province (2022CFB896).
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interests: Authors state no conflict of interest.
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Informed consent: Informed consent was obtained from all individuals included in this study.
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Ethical approval: The local Institutional Review Board deemed the study exempt from review.
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Artikel in diesem Heft
- Frontmatter
- Review
- A review: strategies to reduce infection in tantalum and its derivative applied to implants
- Research Articles
- Biomechanical analysis of different fixed dental restorations on short implants: a finite element study
- Highly sensitive temperature sensor using one-dimensional Bragg Reflector for biomedical applications
- Synchronisation of wearable inertial measurement units based on magnetometer data
- Multiple ECG segments denoising autoencoder model
- Heart sound classification based on equal scale frequency cepstral coefficients and deep learning
- Towards a versatile mental workload modeling using neurometric indices
- An improved multi-source domain adaptation network for inter-subject mental fatigue detection based on DANN
Artikel in diesem Heft
- Frontmatter
- Review
- A review: strategies to reduce infection in tantalum and its derivative applied to implants
- Research Articles
- Biomechanical analysis of different fixed dental restorations on short implants: a finite element study
- Highly sensitive temperature sensor using one-dimensional Bragg Reflector for biomedical applications
- Synchronisation of wearable inertial measurement units based on magnetometer data
- Multiple ECG segments denoising autoencoder model
- Heart sound classification based on equal scale frequency cepstral coefficients and deep learning
- Towards a versatile mental workload modeling using neurometric indices
- An improved multi-source domain adaptation network for inter-subject mental fatigue detection based on DANN
