  Mental disorders are among the leading causes of disability worldwide. The
first step in treating these conditions is to obtain an accurate diagnosis, but
the absence of established clinical tests makes this task challenging. 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 to
the diagnosis of neurological disorders.
