Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: EEG functional connectivity and deep learning for automatic diagnosis of
  brain disorders: Alzheimer's disease and schizophrenia   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.

20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
16
The categories of this paper are: stat
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol