  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.
