Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction

Abstract

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 8.8% improvement in average accuracy of classification, and demonstrate adaptability to a broader range of subjects.

Accepted for publication by IEEE Signal Processing Letters

References in corpus (3)

• Fader Networks: Manipulating Images by Sliding Attributes
• Adversarial Deep Learning in EEG Biometrics
• HANDS: A Multimodal Dataset for Modeling Towards Human Grasp Intent Inference in Prosthetic Hands

Cited by in corpus (3)

• EEG Decoding for Datasets with Heterogenous Electrode Configurations using Transfer Learning Graph Neural Networks
• Towards Cross-Subject EMG Pattern Recognition via Dual-Branch Adversarial Feature Disentanglement
• Universal Physiological Representation Learning with Soft-Disentangled Rateless Autoencoders