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    <language>eng</language>
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    <title language="eng">Decoding Dystonia: unveiling neural patterns with interpretable EEG-Based Machine Learning</title>
    <abstract language="eng">Dystonia has a multifaceted and complex pathogenesis. Current diagnostic proce-dures, which focus primarily on clinical signs, may lack accuracy due to the variable presentationsof different dystonia types. There is a need for objective, interpretable, and non-invasive diagnostictools. This study aims to develop an interpretable electroencephalography (EEG)-basedmachine learning (ML) and deep learning (DL) approach to distinguish between focal upper limbdystonia (ULD), cervical dystonia (CD), and healthy controls (HC). EEG data were recorded during resting-state, writing-from-memory, and finger-tapping tasks. The EEG signals were segmented into windows to generate connectivity matricesusing various pairwise correlation metrics. Machine learning models were trained to classify thegroups, with performance evaluated using accuracy and area under the curve (AUC) metrics. Our approach achieved accuracy and AUC scores close to 100%. Transfer entropyemerged as the most effective connectivity metric, revealing altered brain connections in dystonia.Complex network measures outperformed traditional EEG features, highlighting the relevance offunctional connectivity. Resting-state EEG showed the highest classification performance for ULD,suggesting strong diagnostic potential. Conclusions: This study provides the first machine learning-based comparison between differenttypes of dystonia, introduces novel cervical dystonia EEG data, and yields medically interpretableinsights into altered brain connectivity. The findings enhance our understanding of dystonia and support using EEG as alow-cost, interpretable tool for diagnosing and developing brain-machine interfaces.</abstract>
    <identifier type="doi">https://doi.org/10.21203/rs.3.rs-7483388/v1</identifier>
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Current diagnostic proce-dures, which focus primarily on clinical signs, may lack accuracy due to the variable presentationsof different dystonia types. There is a need for objective, interpretable, and non-invasive diagnostictools. This study aims to develop an interpretable electroencephalography (EEG)-basedmachine learning (ML) and deep learning (DL) approach to distinguish between focal upper limbdystonia (ULD), cervical dystonia (CD), and healthy controls (HC). EEG data were recorded during resting-state, writing-from-memory, and finger-tapping tasks. The EEG signals were segmented into windows to generate connectivity matricesusing various pairwise correlation metrics. Machine learning models were trained to classify thegroups, with performance evaluated using accuracy and area under the curve (AUC) metrics.  Our approach achieved accuracy and AUC scores close to 100%. Transfer entropyemerged as the most effective connectivity metric, revealing altered brain connections in dystonia.Complex network measures outperformed traditional EEG features, highlighting the relevance offunctional connectivity. Resting-state EEG showed the highest classification performance for ULD,suggesting strong diagnostic potential. Conclusions: This study provides the first machine learning-based comparison between differenttypes of dystonia, introduces novel cervical dystonia EEG data, and yields medically interpretableinsights into altered brain connectivity. The findings enhance our understanding of dystonia and support using EEG as alow-cost, interpretable tool for diagnosing and developing brain-machine interfaces.&lt;\/p&gt;","DOI":"10.21203\/rs.3.rs-7483388\/v1","type":"posted-content","created":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T16:33:37Z","timestamp":1758126817000},"source":"Crossref","is-referenced-by-count":0,"title":["Decoding Dystonia: unveiling neural patterns with interpretable EEG-Based Machine Learning"],"prefix":"10.21203","author":[{"given":"Caroline","family":"Alves","sequence":"first","affiliation":[{"name":"Aschaffenburg University of Applied Sciences"}]},{"given":"Artur Jos\u00e9 Marques","family":"Paulo","sequence":"additional","affiliation":[{"name":"Hospital Israelita Albert Einstein"}]},{"given":"Danilo Donizete","family":"de Faria","sequence":"additional","affiliation":[{"name":"Federal University of S\u00e3o Paulo"}]},{"given":"Jo\u00e3o Ricardo","family":"Sato","sequence":"additional","affiliation":[{"name":"Universidade Federal do ABC"}]},{"given":"Vanderci","family":"Borges","sequence":"additional","affiliation":[{"name":"Federal University of S\u00e3o Paulo"}]},{"given":"Sonia de Azevedo","family":"Silva","sequence":"additional","affiliation":[{"name":"Federal University of S\u00e3o Paulo"}]},{"given":"Henrique Ballalai","family":"Ferraz","sequence":"additional","affiliation":[{"name":"Federal University of S\u00e3o Paulo"}]},{"given":"Francisco A.","family":"Rodrigues","sequence":"additional","affiliation":[{"name":"Universidade de S\u00e3o Paulo"}]},{"given":"Christiane","family":"Thielemann","sequence":"additional","affiliation":[{"name":"Aschaffenburg University of Applied Sciences"}]},{"given":"Michael","family":"Moeckel","sequence":"additional","affiliation":[{"name":"Aschaffenburg University of Applied Sciences"}]},{"given":"Patricia de Carvalho","family":"Aguiar","sequence":"additional","affiliation":[{"name":"Federal University of S\u00e3o Paulo"}]}],"member":"297","container-title":[],"original-title":[],"link":[{"URL":"https:\/\/www.researchsquare.com\/article\/rs-7483388\/v1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.researchsquare.com\/article\/rs-7483388\/v1.html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T16:33:41Z","timestamp":1758126821000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.researchsquare.com\/article\/rs-7483388\/v1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,17]]},"references-count":0,"URL":"https:\/\/doi.org\/10.21203\/rs.3.rs-7483388\/v1","relation":{},"subject":[],"published":{"date-parts":[[2025,9,17]]},"subtype":"preprint"}}</enrichment>
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    <author>Caroline Alves</author>
    <author>Artur José Marques Paulo</author>
    <author>Danilo Donizete de Faria</author>
    <author>João Ricardo Sato</author>
    <author>Vanderci Borges</author>
    <author>Sonia de Azevedo Silva</author>
    <author>Henrique Ballalai Ferraz</author>
    <author>Francisco A. Rodrigues</author>
    <author>Christiane Thielemann</author>
    <author>Michael Moeckel</author>
    <author>Patricia de Carvalho Aguiar</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Dystonie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Elektroencephalographie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Maschinelles Lernen</value>
    </subject>
    <collection role="institutes" number="">BIOMEMS Lab</collection>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
  </doc>
  <doc>
    <id>2541</id>
    <completedYear>2025</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>194</volume>
    <type>article</type>
    <publisherName>Elsevier BV</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
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    <title language="eng">Revealing patterns in major depressive disorder with machine learning and networks</title>
    <abstract language="eng">Major depressive disorder (MDD) is a multifaceted condition that affects millions of people worldwide and is a leading cause of disability. There is an urgent need for an automated and objective method to detect MDD due to the limitations of traditional diagnostic approaches. In this paper, we propose a methodology based on machine and deep learning to classify patients with MDD and identify altered functional connectivity patterns from EEG data. We compare several connectivity metrics and machine learning algorithms. Complex network measures are used to identify structural brain abnormalities in MDD. Using Spearman correlation for network construction and the SVM classifier, we verify that it is possible to identify MDD patients with high accuracy, exceeding literature results. The SHAP (SHAPley Additive Explanations) summary plot highlights the importance of C4-F8 connections and also reveals dysfunction in certain brain areas and hyperconnectivity in others. Despite the lower performance of the complex network measures for the classification problem, assortativity was found to be a promising biomarker. Our findings suggest that understanding and diagnosing MDD may be aided by the use of machine learning methods and complex networks.</abstract>
    <parentTitle language="eng">Chaos, Solitons &amp; Fractals</parentTitle>
    <identifier type="issn">0960-0779</identifier>
    <identifier type="doi">https://doi.org/10.1016/j.chaos.2025.116163</identifier>
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    <author>Loriz Francisco Sallum</author>
    <author>Caroline L. Alves</author>
    <author>Thaise Graziele L de O Toutain</author>
    <author>Joel Augusto Moura Porto</author>
    <author>Christiane Thielemann</author>
    <author>Francisco A. Rodrigues</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Depression</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Maschinelles Lernen</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>
  <doc>
    <id>2289</id>
    <completedYear>2024</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>17</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>2024</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-06-09</completedDate>
    <publishedDate>2024-06-09</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Revealing patterns in major depressive disorder with machine learning and networks</title>
    <abstract language="eng">Major depressive disorder (MDD) is a multifaceted condition that affects millions of people worldwide and is a leading cause of disability. There is an urgent need for an automated and objective method to detect MDD due to the limitations of traditional diagnostic approaches. In this paper, we propose a methodology based on machine and deep learning to classify patients with MDD and identify altered functional connectivity patterns from EEG data. We compare several connectivity metrics and machine learning algorithms. Complex network measures are used to identify structural brain abnormalities in MDD. Using Spearman correlation for network construction and the SVM classifier, we verify that it is possible to identify MDD patients with high accuracy, exceeding literature results. The SHAP (SHAPley Additive Explanations) summary plot highlights the importance of C4-F8 connections and also reveals dysfunction in certain brain areas and hyperconnectivity in others. Despite the lower performance of the complex network measures for the classification problem, assortativity was found to be a promising biomarker. Our findings suggest that understanding and diagnosing MDD may be aided by the use of machine learning methods and complex networks.</abstract>
    <parentTitle language="eng">medrxiv</parentTitle>
    <identifier type="doi">doi.org/10.1101/2024.06.07.24308619</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Loriz Francisco Sallum</author>
    <author>Caroline L. Alves</author>
    <author>Christiane Thielemann</author>
    <author>Francisco A. Rodrigues</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Depression</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Elektroencephalographie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Maschinelles Lernen</value>
    </subject>
    <collection role="institutes" number="">BIOMEMS Lab</collection>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
  </doc>
  <doc>
    <id>2072</id>
    <completedYear>2022</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>26</pageLast>
    <pageNumber/>
    <edition/>
    <issue>12</issue>
    <volume>2022</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-12-16</completedDate>
    <publishedDate>2022-12-16</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments</title>
    <abstract language="eng">Ayahuasca is a blend of Amazonian plants that has been used for traditional medicine by&#13;
the inhabitants of this region for hundreds of years. Furthermore, this plant has been demon� strated to be a viable therapy for a variety of neurological and mental diseases. EEG experi� ments have found specific brain regions that changed significantly due to ayahuasca. Here,&#13;
we used an EEG dataset to investigate the ability to automatically detect changes in brain&#13;
activity using machine learning and complex networks. Machine learning was applied at&#13;
three different levels of data abstraction: (A) the raw EEG time series, (B) the correlation of&#13;
the EEG time series, and (C) the complex network measures calculated from (B). Further, at&#13;
the abstraction level of (C), we developed new measures of complex networks relating to&#13;
community detection. As a result, the machine learning method was able to automatically&#13;
detect changes in brain activity, with case (B) showing the highest accuracy (92%), followed&#13;
by (A) (88%) and (C) (83%), indicating that connectivity changes between brain regions are&#13;
more important for the detection of ayahuasca. The most activated areas were the frontal&#13;
and temporal lobe, which is consistent with the literature. F3 and PO4 were the most impor� tant brain connections, a significant new discovery for psychedelic literature. This connec� tion may point to a cognitive process akin to face recognition in individuals during&#13;
ayahuasca-mediated visual hallucinations. Furthermore, closeness centrality and assorta� tivity were the most important complex network measures. These two measures are also&#13;
associated with diseases such as Alzheimer’s disease, indicating a possible therapeutic&#13;
mechanism. Moreover, the new measures were crucial to the predictive model and sug� gested larger brain communities associated with the use of ayahuasca. This suggests that&#13;
the dissemination of information in functional brain networks is slower when this drug is&#13;
present. Overall, our methodology was able to automatically detect changes in brain activity&#13;
during ayahuasca consumption and interpret how these psychedelics alter brain networks,&#13;
as well as provide insights into their mechanisms of action</abstract>
    <parentTitle language="eng">PLOS ONE</parentTitle>
    <identifier type="doi">https://doi.org/10.1371/journal. pone.0277257</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Caroline L. Alves</author>
    <author>Rubens G. Cury</author>
    <author>Kirstin Roster</author>
    <author>Aruane M. Pineda</author>
    <author>Francisco A. Rodrigues</author>
    <author>Christiane Thielemann</author>
    <author>Manuel Ciba</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Ayahuasca</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Elektroencephalographie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Alzheimerkrankheit</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Gehirn</value>
    </subject>
    <collection role="institutes" number="">BIOMEMS Lab</collection>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
    <file>https://opus4.kobv.de/opus4-h-ab/files/2072/journal.pone.0277257.pdf</file>
  </doc>
  <doc>
    <id>2062</id>
    <completedYear>2022</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>e37</pageFirst>
    <pageLast>e47</pageLast>
    <pageNumber/>
    <edition/>
    <issue>137</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="deu">Functional connectivity and convolutional neural networks for automatic classification of EEG data</title>
    <parentTitle language="eng">Clinical Neurophysiology</parentTitle>
    <identifier type="doi">https://doi.org/10.1016/j.clinph.2022.01.086</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Caroline L. Alves</author>
    <author>Lennart Wissel</author>
    <author>Philipp Capetian</author>
    <author>Christiane Thielemann</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Elektroencephalographie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Neuronales Netz</value>
    </subject>
    <collection role="institutes" number="">BIOMEMS Lab</collection>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
  </doc>
  <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>
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    <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>
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