Universal Physiological Representation Learning with Soft-Disentangled Rateless Autoencoders

Mo Han*, Ozan Özdenizci, Toshiaki Koike-Akino, Ye Wang, Deniz Erdogmus

*Korrespondierende/r Autor/in für diese Arbeit

Publikation: Beitrag in einer FachzeitschriftArtikel

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.
Originalspracheenglisch
Aufsatznummer9368997
Seiten (von - bis)2928-2937
Seitenumfang10
FachzeitschriftIEEE Journal of Biomedical and Health Informatics
Jahrgang25
Ausgabenummer8
Frühes Online-Datum3 Mär 2021
DOIs
PublikationsstatusVeröffentlicht - Aug 2021

ASJC Scopus subject areas

  • !!Health Information Management
  • !!Electrical and Electronic Engineering
  • Biotechnology
  • !!Computer Science Applications

Fields of Expertise

  • Human- & Biotechnology
  • Information, Communication & Computing

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