Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction

• Published in: IEEE signal processing letters Vol. 27; pp. 1565 - 1569
• Main Authors: Han, Mo, Ozdenizci, Ozan, Wang, Ye, Koike-Akino, Toshiaki, Erdogmus, Deniz
• Format: Journal Article
• Language: English
• Published: United States IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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
• DOI: 10.1109/LSP.2020.3020215

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.