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dc.contributor.authorVialatte, Francois B.en_US
dc.contributor.authorSole-Casals, Jordien_US
dc.contributor.authorDauwels, Justin H. G.en_US
dc.contributor.authorMaurice, Moniqueen_US
dc.contributor.authorCichocki, Andrzejen_US
dc.date.accessioned2009-10-19T13:35:23Z
dc.date.available2009-10-19T13:35:23Z
dc.date.issued2009-05en_US
dc.date.submitted2008-11en_US
dc.identifier.issn1471-2202en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/49468
dc.description.abstractBackground: oscillatory activity, which can be separated in background and oscillatory burst pattern activities, is supposed to be representative of local synchronies of neural assemblies. Oscillatory burst events should consequently play a specific functional role, distinct from background EEG activity – especially for cognitive tasks (e.g. working memory tasks), binding mechanisms and perceptual dynamics (e.g. visual binding), or in clinical contexts (e.g. effects of brain disorders). However extracting oscillatory events in single trials, with a reliable and consistent method, is not a simple task. Results: in this work we propose a user-friendly stand-alone toolbox, which models in a reasonable time a bump time-frequency model from the wavelet representations of a set of signals. The software is provided with a Matlab toolbox which can compute wavelet representations before calling automatically the stand-alone application. Conclusion: The tool is publicly available as a freeware at the address: http:// www.bsp.brain.riken.jp/bumptoolbox/toolbox_home.htmlen_US
dc.language.isoen_USen_US
dc.publisherBioMed Central Ltd.en_US
dc.relation.isversionofhttp://dx.doi.org/10.1186/1471-2202-10-46en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttp://creativecommons.org/licenses/by/2.0en_US
dc.sourcePublisheren_US
dc.titleBump time-frequency toolbox: a toolbox for time-frequency oscillatory bursts extraction in electrophysiological signalsen_US
dc.typeArticleen_US
dc.identifier.citationVialatte, Francois et al. “Bump time-frequency toolbox: a toolbox for time-frequency oscillatory bursts extraction in electrophysiological signals.” BMC Neuroscience 10.1 (2009): 46.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Laboratory for Information and Decision Systemsen_US
dc.contributor.approverDauwels, Justin H. G.en_US
dc.contributor.mitauthorDauwels, Justin H. G.en_US
dc.relation.journalBMC Neuroscienceen_US
dc.eprint.versionFinal published versionen_US
dc.identifier.pmid19432999en_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsVialatte, Francois B; Sole-Casals, Jordi; Dauwels, Justin; Maurice, Monique; Cichocki, Andrzejen
mit.licensePUBLISHER_CCen_US
mit.metadata.statusComplete


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