Universal Physiological Representation Learning With Soft-Disentangled Rateless Autoencoders | IEEE Journals & Magazine | IEEE Xplore

Universal Physiological Representation Learning With Soft-Disentangled Rateless Autoencoders


Abstract:

Human computer interaction (HCI) involves a multidisciplinary fusion of technologies, through which the control of external devices could be achieved by monitoring physio...Show More

Abstract:

Human computer interaction (HCI) involves a multidisciplinary fusion of technologies, through which the control of external devices could be achieved by monitoring physiological status of users. However, physiological biosignals often vary across users and recording sessions due to unstable physical/mental conditions and task-irrelevant activities. To deal with this challenge, we propose a method of adversarial feature encoding with the concept of a Rateless Autoencoder (RAE), in order to exploit disentangled, nuisance-robust, and universal representations. We achieve a good trade-off between user-specific and task-relevant features by making use of the stochastic disentanglement of the latent representations by adopting additional adversarial networks. The proposed model is applicable to a wider range of unknown users and tasks as well as different classifiers. Results on cross-subject transfer evaluations show the advantages of the proposed framework, with up to an 11.6% improvement in the average subject-transfer classification accuracy.
Published in: IEEE Journal of Biomedical and Health Informatics ( Volume: 25, Issue: 8, August 2021)
Page(s): 2928 - 2937
Date of Publication: 03 March 2021

ISSN Information:

PubMed ID: 33657004

Funding Agency:


References

References is not available for this document.