  The increasing access to brain signal data using electroencephalography
creates new opportunities to study electrophysiological brain activity and
perform ambulatory diagnoses of neuronal diseases. This work proposes a
pairwise distance learning approach for Schizophrenia classification relying on
the spectral properties of the signal. Given the limited number of observations
(i.e. the case and/or control individuals) in clinical trials, we propose a
Siamese neural network architecture to learn a discriminative feature space
from pairwise combinations of observations per channel. In this way, the
multivariate order of the signal is used as a form of data augmentation,
further supporting the network generalization ability. Convolutional layers
with parameters learned under a cosine contrastive loss are proposed to
adequately explore spectral images derived from the brain signal. Results on a
case-control population show that the features extracted using the proposed
neural network lead to an improved Schizophrenia diagnosis (+10pp in accuracy
and sensitivity) against spectral features, thus suggesting the existence of
non-trivial, discriminative electrophysiological brain patterns.
