  Electroencephalography (EEG) is a prominent non-invasive neuroimaging
technique providing insights into brain function. Unfortunately, EEG data
exhibit a high degree of noise and variability across subjects hampering
generalizable signal extraction. Therefore, a key aim in EEG analysis is to
extract the underlying neural activation (content) as well as to account for
the individual subject variability (style). We hypothesize that the ability to
convert EEG signals between tasks and subjects requires the extraction of
latent representations accounting for content and style. Inspired by recent
advancements in voice conversion technologies, we propose a novel contrastive
split-latent permutation autoencoder (CSLP-AE) framework that directly
optimizes for EEG conversion. Importantly, the latent representations are
guided using contrastive learning to promote the latent splits to explicitly
represent subject (style) and task (content). We contrast CSLP-AE to
conventional supervised, unsupervised (AE), and self-supervised (contrastive
learning) training and find that the proposed approach provides favorable
generalizable characterizations of subject and task. Importantly, the procedure
also enables zero-shot conversion between unseen subjects. While the present
work only considers conversion of EEG, the proposed CSLP-AE provides a general
framework for signal conversion and extraction of content (task activation) and
style (subject variability) components of general interest for the modeling and
analysis of biological signals.
