<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>2060</id>
    <completedYear>2022</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>13</pageLast>
    <pageNumber/>
    <edition/>
    <issue>3</issue>
    <volume>2022</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-11-17</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer’s disease and schizophrenia</title>
    <abstract language="eng">Mental disorders are among the leading causes of disability worldwide. The first step in treating&#13;
these conditions is to obtain an accurate diagnosis. Machine learning algorithms can provide a&#13;
possible solution to this problem, as we describe in this work. We present a method for the&#13;
automatic diagnosis of mental disorders based on the matrix of connections obtained from EEG&#13;
time series and deep learning. We show that our approach can classify patients with Alzheimer’s&#13;
disease and schizophrenia with a high level of accuracy. The comparison with the traditional cases,&#13;
that use raw EEG time series, shows that our method provides the highest precision. Therefore, the&#13;
application of deep neural networks on data from brain connections is a very promising method&#13;
for the diagnosis of neurological disorders.</abstract>
    <parentTitle language="eng">Journal of Physics: complexity</parentTitle>
    <identifier type="doi">DOI 10.1088/2632-072X/ac5f8d</identifier>
    <enrichment key="copyright">0</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Caroline L. Alves</author>
    <author>Aruane M. Pineda</author>
    <author>Kirstin Roster</author>
    <author>Christiane Thielemann</author>
    <author>Francisco A. Rodrigues</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>complex networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine learning</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Hirnfunktionsstörung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Alzheimerkrankheit</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Schizophrenie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Elektroencephalographie</value>
    </subject>
    <collection role="institutes" number="">BIOMEMS Lab</collection>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
  </doc>
</export-example>
