  Despite extensive standardization, diagnostic interviews for mental health
disorders encompass substantial subjective judgment. Previous studies have
demonstrated that EEG-based neural measures can function as reliable objective
correlates of depression, or even predictors of depression and its course.
However, their clinical utility has not been fully realized because of 1) the
lack of automated ways to deal with the inherent noise associated with EEG data
at scale, and 2) the lack of knowledge of which aspects of the EEG signal may
be markers of a clinical disorder. Here we adapt an unsupervised pipeline from
the recent deep representation learning literature to address these problems by
1) learning a disentangled representation using $\beta$-VAE to denoise the
signal, and 2) extracting interpretable features associated with a sparse set
of clinical labels using a Symbol-Concept Association Network (SCAN). We
demonstrate that our method is able to outperform the canonical hand-engineered
baseline classification method on a number of factors, including participant
age and depression diagnosis. Furthermore, our method recovers a representation
that can be used to automatically extract denoised Event Related Potentials
(ERPs) from novel, single EEG trajectories, and supports fast supervised
re-mapping to various clinical labels, allowing clinicians to re-use a single
EEG representation regardless of updates to the standardized diagnostic system.
Finally, single factors of the learned disentangled representations often
correspond to meaningful markers of clinical factors, as automatically detected
by SCAN, allowing for human interpretability and post-hoc expert analysis of
the recommendations made by the model.
