  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.
