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Article: Linear spatial integration for single-trial detection in encephalography

TitleLinear spatial integration for single-trial detection in encephalography
Authors
Issue Date2002
Citation
NeuroImage, 2002, v. 17, n. 1, p. 223-230 How to Cite?
AbstractConventional analysis of electroencephalography (EEG) and magnetoencephalography (MEG) often relies on averaging over multiple trials to extract statistically relevant differences between two or more experimental conditions. In this article we demonstrate single-trial detection by linearly integrating information over multiple spatially distributed sensors within a predefined time window. We report an average, single-trial discrimination performance of Az ≈ 0.80 and fraction correct between 0.70 and 0.80, across three distinct encephalographic data sets. We restrict our approach to linear integration, as it allows the computation of a spatial distribution of the discriminating component activity. In the present set of experiments the resulting component activity distributions are shown to correspond to the functional neuroanatomy consistent with the task (e.g., contralateral sensory-motor cortex and anterior cingulate). Our work demonstrates how a purely data-driven method for learning an optimal spatial weighting of encephalographic activity can be validated against the functional neuroanatomy. © 2002 Elsevier Science (USA).
Persistent Identifierhttp://hdl.handle.net/10722/228017
ISSN
2015 Impact Factor: 5.463
2015 SCImago Journal Rankings: 4.464

 

DC FieldValueLanguage
dc.contributor.authorParra, Lucas-
dc.contributor.authorAlvino, Chris-
dc.contributor.authorTang, Akaysha-
dc.contributor.authorPearlmutter, Barak-
dc.contributor.authorYeung, Nick-
dc.contributor.authorOsman, Allen-
dc.contributor.authorSajda, Paul-
dc.date.accessioned2016-08-01T06:44:59Z-
dc.date.available2016-08-01T06:44:59Z-
dc.date.issued2002-
dc.identifier.citationNeuroImage, 2002, v. 17, n. 1, p. 223-230-
dc.identifier.issn1053-8119-
dc.identifier.urihttp://hdl.handle.net/10722/228017-
dc.description.abstractConventional analysis of electroencephalography (EEG) and magnetoencephalography (MEG) often relies on averaging over multiple trials to extract statistically relevant differences between two or more experimental conditions. In this article we demonstrate single-trial detection by linearly integrating information over multiple spatially distributed sensors within a predefined time window. We report an average, single-trial discrimination performance of Az ≈ 0.80 and fraction correct between 0.70 and 0.80, across three distinct encephalographic data sets. We restrict our approach to linear integration, as it allows the computation of a spatial distribution of the discriminating component activity. In the present set of experiments the resulting component activity distributions are shown to correspond to the functional neuroanatomy consistent with the task (e.g., contralateral sensory-motor cortex and anterior cingulate). Our work demonstrates how a purely data-driven method for learning an optimal spatial weighting of encephalographic activity can be validated against the functional neuroanatomy. © 2002 Elsevier Science (USA).-
dc.languageeng-
dc.relation.ispartofNeuroImage-
dc.titleLinear spatial integration for single-trial detection in encephalography-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1006/nimg.2002.1212-
dc.identifier.pmid12482079-
dc.identifier.scopuseid_2-s2.0-0036743145-
dc.identifier.volume17-
dc.identifier.issue1-
dc.identifier.spage223-
dc.identifier.epage230-

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