# Universal Physiological Representation Learning with Soft-Disentangled Rateless Autoencoders

arXiv: https://arxiv.org/abs/2009.13453 · HTML: https://mlsanity.com/paper/2009.13453/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2020.

## Built on by (most cited first, 7 of 7)

- [EEG2Vec: Learning Affective EEG Representations via Variational Autoencoders](https://mlsanity.com/paper/2207.08002/) (2022)
- [Exploiting Multiple EEG Data Domains with Adversarial Learning](https://mlsanity.com/paper/2204.07777/) (2022)
- [Transfer Learning on Electromyography (EMG) Tasks: Approaches and Beyond](https://mlsanity.com/paper/2210.06295/) (2022)
- [AutoTransfer: Subject Transfer Learning with Censored Representations on Biosignals Data](https://mlsanity.com/paper/2112.09796/) (2021)
- [quEEGNet: Quantum AI for Biosignal Processing](https://mlsanity.com/paper/2210.00864/) (2022)
- [TS-MoCo: Time-Series Momentum Contrast for Self-Supervised Physiological Representation Learning](https://mlsanity.com/paper/2306.06522/) (2023)
- [Omni-modal decomposition autoencoders learn full-stack wearable disentangled representations](https://mlsanity.com/paper/2608.07385/) (2026)

## Abstract (arXiv)

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.