Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine learning approaches in Detecting the Depression from
  Resting-state Electroencephalogram (EEG): A Review Study   In this paper, we aimed at reviewing several different approaches present
today in the search for more accurate diagnostic and treatment management in
mental healthcare. Our focus is on mood disorders, and in particular on the
major depressive disorder (MDD). We are reviewing and discussing findings based
on neuroimaging studies (MRI and fMRI) first to get the impression of the body
of knowledge about the anatomical and functional differences in depression.
Then, we are focusing on less expensive data-driven approach, applicable for
everyday clinical practice, in particular, those based on
electroencephalographic (EEG) recordings. Among those studies utilizing EEG, we
are discussing a group of applications used for detecting of depression based
on the resting state EEG (detection studies) and interventional studies (using
stimulus in their protocols or aiming to predict the outcome of therapy). We
conclude with a discussion and review of guidelines to improve the reliability
of developed models that could serve improvement of diagnostic of depression in
psychiatry.

17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex