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Mental disorders are among the leading causes of disability worldwide. Thefirst step in treating these conditions is to obtain an accurate diagnosis, butthe absence of established clinical tests makes this task challenging. Machinelearning algorithms can provide a possible solution to this problem, as wedescribe in this work. We present a method for the automatic diagnosis ofmental disorders based on the matrix of connections obtained from EEG timeseries and deep learning. We show that our approach can classify patients withAlzheimer's disease and schizophrenia with a high level of accuracy. Thecomparison with the traditional cases, that use raw EEG time series, shows thatour method provides the highest precision. Therefore, the application of deepneural networks on data from brain connections is a very promising method tothe diagnosis of neurological disorders.

EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer's disease and schizophrenia