- Mental disorders are among the leading causes of disability worldwide. The first step in treating
these conditions is to obtain an accurate diagnosis. Machine learning algorithms can provide a
possible solution to this problem, as we describe in this work. We present a method for the
automatic diagnosis of mental disorders based on the matrix of connections obtained from EEG
time series and deep learning. We show that our approach can classify patients with Alzheimer’s
disease and schizophrenia with a high level of accuracy. The comparison with the traditional cases,
that use raw EEG time series, shows that our method provides the highest precision. Therefore, the
application of deep neural networks on data from brain connections is a very promising method
for the diagnosis of neurological disorders.