MEG/EEG Source Reconstruction, Statistical Evaluation, and Visualization with NUTMEG

dc.contributor.authorDalal, Sarang S.
dc.contributor.authorZumer, Johannadeu
dc.contributor.authorGuggisberg, Adriandeu
dc.contributor.authorTrumpis, Michaeldeu
dc.contributor.authorWong, Daniel
dc.contributor.authorSekihara, Kensukedeu
dc.contributor.authorNagarajan, Srikantandeu
dc.date.accessioned2011-09-07T05:48:51Zdeu
dc.date.available2011-09-07T05:48:51Zdeu
dc.date.issued2011
dc.description.abstractNUTMEG is a source analysis toolbox geared towards cognitive neuroscience researchers using MEG and EEG, including intracranial recordings. Evoked and unaveraged data can be imported to the toolbox for source analysis in either the time or time-frequency domains. NUTMEG offers several variants of adaptive beamformers, probabilistic reconstruction algorithms, as well as minimum-norm techniques to generate functional maps of spatiotemporal neural source activity. Lead fields can be calculated from single and overlapping sphere head models or imported from other software. Group averages and statistics can be calculated as well. In addition to data analysis tools, NUTMEG provides a unique and intuitive graphical interface for visualization of results. Source analyses can be superimposed onto a structural MRI or headshape to provide a convenient visual correspondence to anatomy. These results can also be navigated interactively, with the spatial maps and source time series or spectrogram linked accordingly. Animations can be generated to view the evolution of neural activity over time. NUTMEG can also display brain renderings and perform spatial normalization of functional maps using SPM's engine. As a MATLAB package, the end user may easily link with other toolboxes or add customized functions.eng
dc.description.versionpublished
dc.identifier.citationFirst publ. in: Computational Intelligence and Neuroscience ; 2011. - 758973deu
dc.identifier.doi10.1155/2011/758973deu
dc.identifier.pmid21437174
dc.identifier.ppn349959803deu
dc.identifier.urihttp://kops.uni-konstanz.de/handle/123456789/12628
dc.language.isoengdeu
dc.legacy.dateIssued2011-09-07deu
dc.rightsterms-of-usedeu
dc.rights.urihttps://rightsstatements.org/page/InC/1.0/deu
dc.subject.ddc150deu
dc.subject.gndElektroencephalographiedeu
dc.subject.gndMagnetoencephalographiedeu
dc.titleMEG/EEG Source Reconstruction, Statistical Evaluation, and Visualization with NUTMEGeng
dc.typeJOURNAL_ARTICLEdeu
dspace.entity.typePublication
kops.citation.bibtex
@article{Dalal2011MEGEE-12628,
  year={2011},
  doi={10.1155/2011/758973},
  title={MEG/EEG Source Reconstruction, Statistical Evaluation, and Visualization with NUTMEG},
  volume={2011},
  issn={1687-5265},
  journal={Computational Intelligence and Neuroscience},
  pages={1--17},
  author={Dalal, Sarang S. and Zumer, Johanna and Guggisberg, Adrian and Trumpis, Michael and Wong, Daniel and Sekihara, Kensuke and Nagarajan, Srikantan}
}
kops.citation.iso690DALAL, Sarang S., Johanna ZUMER, Adrian GUGGISBERG, Michael TRUMPIS, Daniel WONG, Kensuke SEKIHARA, Srikantan NAGARAJAN, 2011. MEG/EEG Source Reconstruction, Statistical Evaluation, and Visualization with NUTMEG. In: Computational Intelligence and Neuroscience. 2011, 2011, pp. 1-17. ISSN 1687-5265. eISSN 1687-5273. Available under: doi: 10.1155/2011/758973deu
kops.citation.iso690DALAL, Sarang S., Johanna ZUMER, Adrian GUGGISBERG, Michael TRUMPIS, Daniel WONG, Kensuke SEKIHARA, Srikantan NAGARAJAN, 2011. MEG/EEG Source Reconstruction, Statistical Evaluation, and Visualization with NUTMEG. In: Computational Intelligence and Neuroscience. 2011, 2011, pp. 1-17. ISSN 1687-5265. eISSN 1687-5273. Available under: doi: 10.1155/2011/758973eng
kops.citation.rdf
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/12628">
    <dc:creator>Nagarajan, Srikantan</dc:creator>
    <dc:creator>Zumer, Johanna</dc:creator>
    <dcterms:bibliographicCitation>First publ. in: Computational Intelligence and Neuroscience ; 2011. - 758973</dcterms:bibliographicCitation>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dcterms:abstract xml:lang="eng">NUTMEG is a source analysis toolbox geared towards cognitive neuroscience researchers using MEG and EEG, including intracranial recordings. Evoked and unaveraged data can be imported to the toolbox for source analysis in either the time or time-frequency domains. NUTMEG offers several variants of adaptive beamformers, probabilistic reconstruction algorithms, as well as minimum-norm techniques to generate functional maps of spatiotemporal neural source activity. Lead fields can be calculated from single and overlapping sphere head models or imported from other software. Group averages and statistics can be calculated as well. In addition to data analysis tools, NUTMEG provides a unique and intuitive graphical interface for visualization of results. Source analyses can be superimposed onto a structural MRI or headshape to provide a convenient visual correspondence to anatomy. These results can also be navigated interactively, with the spatial maps and source time series or spectrogram linked accordingly. Animations can be generated to view the evolution of neural activity over time. NUTMEG can also display brain renderings and perform spatial normalization of functional maps using SPM's engine. As a MATLAB package, the end user may easily link with other toolboxes or add customized functions.</dcterms:abstract>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/12628/1/Dalal_MEG.pdf"/>
    <dcterms:issued>2011</dcterms:issued>
    <dc:contributor>Sekihara, Kensuke</dc:contributor>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-09-07T05:48:51Z</dcterms:available>
    <dc:contributor>Guggisberg, Adrian</dc:contributor>
    <dc:contributor>Dalal, Sarang S.</dc:contributor>
    <dc:rights>terms-of-use</dc:rights>
    <dc:creator>Wong, Daniel</dc:creator>
    <dc:creator>Guggisberg, Adrian</dc:creator>
    <dc:contributor>Nagarajan, Srikantan</dc:contributor>
    <dc:creator>Trumpis, Michael</dc:creator>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/12628/1/Dalal_MEG.pdf"/>
    <dc:creator>Dalal, Sarang S.</dc:creator>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-09-07T05:48:51Z</dc:date>
    <dc:contributor>Zumer, Johanna</dc:contributor>
    <dc:contributor>Wong, Daniel</dc:contributor>
    <dc:creator>Sekihara, Kensuke</dc:creator>
    <dcterms:title>MEG/EEG Source Reconstruction, Statistical Evaluation, and Visualization with NUTMEG</dcterms:title>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43"/>
    <dc:contributor>Trumpis, Michael</dc:contributor>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/12628"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43"/>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/52"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/52"/>
    <dc:language>eng</dc:language>
  </rdf:Description>
</rdf:RDF>
kops.description.openAccessopenaccessgreen
kops.flag.knbibliographytrue
kops.identifier.nbnurn:nbn:de:bsz:352-126282deu
kops.sourcefieldComputational Intelligence and Neuroscience. 2011, <b>2011</b>, pp. 1-17. ISSN 1687-5265. eISSN 1687-5273. Available under: doi: 10.1155/2011/758973deu
kops.sourcefield.plainComputational Intelligence and Neuroscience. 2011, 2011, pp. 1-17. ISSN 1687-5265. eISSN 1687-5273. Available under: doi: 10.1155/2011/758973deu
kops.sourcefield.plainComputational Intelligence and Neuroscience. 2011, 2011, pp. 1-17. ISSN 1687-5265. eISSN 1687-5273. Available under: doi: 10.1155/2011/758973eng
kops.submitter.emailsarang.dalal@uni-konstanz.dedeu
relation.isAuthorOfPublication49d8fd2c-e27e-4014-af58-a0b58eaf3a9f
relation.isAuthorOfPublication6d7166a3-23d5-4153-b3ae-c8569e276384
relation.isAuthorOfPublication.latestForDiscovery49d8fd2c-e27e-4014-af58-a0b58eaf3a9f
source.bibliographicInfo.fromPage1
source.bibliographicInfo.toPage17
source.bibliographicInfo.volume2011
source.identifier.eissn1687-5273
source.identifier.issn1687-5265
source.periodicalTitleComputational Intelligence and Neuroscience
temp.target.additionalFachbereich Psychologiedeu

Dateien

Originalbündel

Gerade angezeigt 1 - 1 von 1
Vorschaubild nicht verfügbar
Name:
Dalal_MEG.pdf
Größe:
3.65 MB
Format:
Adobe Portable Document Format
Dalal_MEG.pdf
Dalal_MEG.pdfGröße: 3.65 MBDownloads: 1089

Lizenzbündel

Gerade angezeigt 1 - 1 von 1
Vorschaubild nicht verfügbar
Name:
license.txt
Größe:
1.92 KB
Format:
Plain Text
Beschreibung:
license.txt
license.txtGröße: 1.92 KBDownloads: 0