Universal Physiological Representation Learning with Soft-Disentangled Rateless Autoencoders

Universal Physiological Representation Learning with Soft-Disentangled Rateless Autoencoders

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...

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Bibliographic Details
Main Authors: Erdogmus, D. (Author), Han, M. (Author), Koike-Akino, T. (Author), Ozdenizci, O. (Author), Wang, Y. (Author)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:
Adversarial learning
Adversarial networks
article
Auto encoders
autoencoder
autoencoders
Biosignals
Classification accuracy
classifier
deep learning
disentangled representation
Economic and social effects
human
Human computer interaction
Human computer interaction (HCI)
human experiment
Humans
Learning systems
machine learning
Machine Learning
physiological biosignals
Physiological status
Physiology
soft disentanglement
stochastic bottleneck
stochastic model
Stochastic systems
Task relevant
Trade off
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