Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction

Recent developments in biosignal processing have enabled users to exploit their physiological status for manipulating devices in a reliable and safe manner. One major challenge of physiological sensing lies in the variability of biosignals across different users and tasks. To address this issue, we...

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Vydané v:IEEE signal processing letters Ročník 27; s. 1565 - 1569
Hlavní autori: Han, Mo, Ozdenizci, Ozan, Wang, Ye, Koike-Akino, Toshiaki, Erdogmus, Deniz
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Adversarial deep learning
Biomedical monitoring
Decoding
Feature extraction
Physiology
Representations
Stress
stress assessment
Task analysis
Training
stress assessment
adversarial deep learning
ISSN:1070-9908, 1558-2361
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Shrnutí:Recent developments in biosignal processing have enabled users to exploit their physiological status for manipulating devices in a reliable and safe manner. One major challenge of physiological sensing lies in the variability of biosignals across different users and tasks. To address this issue, we propose an adversarial feature extractor for transfer learning to exploit disentangled universal representations. We consider the trade-off between task-relevant features and user-discriminative information by introducing additional adversary and nuisance networks in order to manipulate the latent representations such that the learned feature extractor is applicable to unknown users and various tasks. Results on cross-subject transfer evaluations exhibit the benefits of the proposed framework, with up to <inline-formula><tex-math notation="LaTeX">8.8\%</tex-math></inline-formula> improvement in average accuracy of classification, and demonstrate adaptability to a broader range of subjects.
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ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2020.3020215