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  <doc>
    <id>2471</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>10</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>14</volume>
    <type>article</type>
    <publisherName>frontiers</publisherName>
    <publisherPlace/>
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    <belongsToBibliography>0</belongsToBibliography>
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    <title language="eng">A Constrained ICA-EMD Model for Group Level fMRI Analysis</title>
    <abstract language="eng">Independent component analysis (ICA), being a data-driven method, has been shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is that it is not, in general, compatible with the analysis of group data. Various techniques have been proposed to overcome this limitation of ICA. In this paper, a novel ICA-based workflow for extracting resting-state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used, in a data-driven manner, to generate reference signals that can be incorporated into a constrained version of ICA (cICA), thereby eliminating the inherent ambiguities of ICA. The results of the proposed workflow are then compared to those obtained by a widely used group ICA approach for fMRI analysis. In this study, we demonstrate that intrinsic modes, extracted by EMD, are suitable to serve as references for cICA. This approach yields typical resting-state patterns that are consistent over subjects. By introducing these reference signals into the ICA, our processing pipeline yields comparable activity patterns across subjects in a mathematically transparent manner. Our approach provides a user-friendly tool to adjust the trade-off between a high similarity across subjects and preserving individual subject features of the independent components.</abstract>
    <parentTitle language="eng">Frontiers in Neuroscience</parentTitle>
    <identifier type="doi">10.3389/fnins.2020.00221</identifier>
    <identifier type="issn">1662-453X</identifier>
    <identifier type="issn">1662-4548</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Simon Wein</author>
    <author>Ana Maria Tomé</author>
    <author>Markus Goldhacker</author>
    <author>Mark W. Greenlee</author>
    <author>Elmar Wolfgang Lang</author>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="oaweg" number="">Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
  <doc>
    <id>5102</id>
    <completedYear/>
    <publishedYear>2015</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>193</pageFirst>
    <pageLast>205</pageLast>
    <pageNumber/>
    <edition/>
    <issue>September</issue>
    <volume>253</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>AMsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">EMDLAB: A toolbox for analysis of single-trial EEG dynamics using empirical mode decomposition</title>
    <abstract language="eng">Background: &#13;
Empirical mode decomposition (EMD) is an empirical data decomposition technique. Recently there is growing interest in applying EMD in the biomedical field.&#13;
&#13;
New method: &#13;
EMDLAB is an extensible plug-in for the EEGLAB toolbox, which is an open software environment for electrophysiological data analysis.&#13;
&#13;
Results: &#13;
EMDLAB can be used to perform, easily and effectively, four common types of EMD: plain EMD, ensemble EMD (EEMD), weighted sliding EMD (wSEMD) and multivariate EMD (MEMD) on EEG data. In addition, EMDLAB is a user-friendly toolbox and closely implemented in the EEGLAB toolbox.&#13;
&#13;
Comparison with existing methods: &#13;
EMDLAB gains an advantage over other open-source toolboxes by exploiting the advantageous visualization capabilities of EEGLAB for extracted intrinsic mode functions (IMFs) and Event-Related Modes (ERMs) of the signal.&#13;
&#13;
Conclusions: &#13;
EMDLAB is a reliable, efficient, and automated solution for extracting and visualizing the extracted IMFs and ERMs by EMD algorithms in EEG study.</abstract>
    <parentTitle language="eng">Journal of Neuroscience Methods</parentTitle>
    <identifier type="pmid">26162614</identifier>
    <identifier type="doi">10.1016/j.jneumeth.2015.06.020</identifier>
    <enrichment key="opus.import.date">2022-08-05T07:16:41+00:00</enrichment>
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    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Markus Goldhacker</author>
    <author>Karema Al-Subari</author>
    <author>Saad Al-Baddai</author>
    <author>Ana Maria Tomé</author>
    <author>Rupert Faltermeier</author>
    <author>Elmar Wolfgang Lang</author>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
  <doc>
    <id>5356</id>
    <completedYear/>
    <publishedYear>2015</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Academic Press</publisherName>
    <publisherPlace>San Diego, CA</publisherPlace>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Early meta-level: deeper understanding of connectivity-states and consequences for state definition</title>
    <parentTitle language="eng">21st Annual Meeting of the Organization for Human Brain Mapping, June 14-18, 2015, Honolulu, Hawaii</parentTitle>
    <identifier type="doi">10.13140/RG.2.1.2561.3929</identifier>
    <enrichment key="opus.import.date">2022-08-24T10:21:08+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Markus Goldhacker</author>
    <author>Ana Maria Tomé</author>
    <author>Mark W. Greenlee</author>
    <author>Elmar Wolfgang Lang</author>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16316">Produktion und Systeme</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
  <doc>
    <id>5364</id>
    <completedYear/>
    <publishedYear>2015</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>movingimage</type>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">EMDLAB-toolbox- tutorial video</title>
    <identifier type="url">https://www.researchgate.net/publication/280492172_EMDLAB-toolbox-_tutorial_video</identifier>
    <enrichment key="opus.import.date">2022-08-24T10:21:08+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Karema Al-Subari</author>
    <author>Saad Al-Baddai</author>
    <author>Ana Maria Tomé</author>
    <author>Markus Goldhacker</author>
    <author>Rupert Faltermeier</author>
    <author>Elmar Wolfgang Lang</author>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
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