  Various hand-crafted features representations of bio-signals rely primarily
on the amplitude or power of the signal in specific frequency bands. The phase
component is often discarded as it is more sample specific, and thus more
sensitive to noise, than the amplitude. However, in general, the phase
component also carries information relevant to the underlying biological
processes. In fact, in this paper we show the benefits of learning the coupling
of both phase and amplitude components of a bio-signal. We do so by introducing
a novel self-supervised learning task, which we call Phase-Swap, that detects
if bio-signals have been obtained by merging the amplitude and phase from
different sources. We show in our evaluation that neural networks trained on
this task generalize better across subjects and recording sessions than their
fully supervised counterpart.
