  Schizophrenia is a complex psychiatric disorder involving changes in thought
patterns, perception, mood, and behavior. The diagnosis of schizophrenia is
challenging and requires that patients show two or more positive symptoms for
at least one month. Delays in identifying this debilitating disorder can impede
a patient ability to receive much needed treatment. Advances in neuroimaging
and machine learning algorithms can facilitate the diagnosis of schizophrenia
and help clinicians to provide an accurate diagnosis of the disease. This paper
presents a methodology for analyzing spectral images of Electroencephalography
collected from patients with schizophrenia using convolutional neural networks.
It also explains how we have developed accurate classifiers employing
Model-Agnostic Meta-Learning and prototypical networks. Such classifiers have
the capacity to distinguish people with schizophrenia from healthy controls
based on their brain activity.
