The increasing access to brain signal data using electroencephalography creates new opportunities to study electrophysiological brain activity and perform ambulatory diagnoses of neurological disorders. This work proposes a pairwise distance learning approach for schizophrenia classification relying on the spectral properties of the signal. To be able to handle clinical trials with a limited number of observations (i.e. case and/or control individuals), 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. The proposed approach for schizophrenia diagnostic was tested on reference clinical trial data under resting-state protocol, achieving 0.95 ± 0.05 accuracy, 0.98 ± 0.02 sensitivity and 0.92 ± 0.07 specificity. Results show that the features extracted using the proposed neural network are remarkably superior than baselines to diagnose schizophrenia (+20pp in accuracy and sensitivity), suggesting the existence of non-trivial electrophysiological brain patterns able to capture discriminative neuroplasticity profiles among individuals. The code is available on Github: https://github.com/DCalhas/siamese_schizophrenia_eeg.
Keywords: Classification Electroencephalography Pairwise learning Schizophrenia