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Article: Single-trial detection in EEG and MEG: Keeping it linear
Title | Single-trial detection in EEG and MEG: Keeping it linear |
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Authors | |
Keywords | Brain-computer interface (BCI) |
Issue Date | 2003 |
Citation | Neurocomputing, 2003, v. 52-54, p. 177-183 How to Cite? |
Abstract | Conventional electroencephalography (EEG) and magnetoencephalography (MEG) analysis often rely on averaging over multiple trials to extract statistically relevant differences between two or more experimental conditions. We demonstrate that by linearly integrating information over multiple spatially distributed sensors within a predefined time window, one can discriminate conditions on a trial-by-trial basis with high accuracy. We restrict ourselves to a linear integration as it allows the computation of a spatial distribution of the discriminating source activity. In the present set of experiments the resulting source activity distributions correspond to functional neuroanatomy consistent with the task (e.g. contralateral sensory-motor cortex and anterior cingulate). © 2003 Elsevier Science B.V. All rights reserved. |
Persistent Identifier | http://hdl.handle.net/10722/228021 |
ISSN | 2023 Impact Factor: 5.5 2023 SCImago Journal Rankings: 1.815 |
DC Field | Value | Language |
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dc.contributor.author | Parra, Lucas | - |
dc.contributor.author | Alvino, Chris | - |
dc.contributor.author | Tang, Akaysha | - |
dc.contributor.author | Pearlmutter, Barak | - |
dc.contributor.author | Yeung, Nick | - |
dc.contributor.author | Osman, Allen | - |
dc.contributor.author | Sajda, Paul | - |
dc.date.accessioned | 2016-08-01T06:44:59Z | - |
dc.date.available | 2016-08-01T06:44:59Z | - |
dc.date.issued | 2003 | - |
dc.identifier.citation | Neurocomputing, 2003, v. 52-54, p. 177-183 | - |
dc.identifier.issn | 0925-2312 | - |
dc.identifier.uri | http://hdl.handle.net/10722/228021 | - |
dc.description.abstract | Conventional electroencephalography (EEG) and magnetoencephalography (MEG) analysis often rely on averaging over multiple trials to extract statistically relevant differences between two or more experimental conditions. We demonstrate that by linearly integrating information over multiple spatially distributed sensors within a predefined time window, one can discriminate conditions on a trial-by-trial basis with high accuracy. We restrict ourselves to a linear integration as it allows the computation of a spatial distribution of the discriminating source activity. In the present set of experiments the resulting source activity distributions correspond to functional neuroanatomy consistent with the task (e.g. contralateral sensory-motor cortex and anterior cingulate). © 2003 Elsevier Science B.V. All rights reserved. | - |
dc.language | eng | - |
dc.relation.ispartof | Neurocomputing | - |
dc.subject | Brain-computer interface (BCI) | - |
dc.title | Single-trial detection in EEG and MEG: Keeping it linear | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.scopus | eid_2-s2.0-0037781982 | - |
dc.identifier.volume | 52-54 | - |
dc.identifier.spage | 177 | - |
dc.identifier.epage | 183 | - |
dc.identifier.issnl | 0925-2312 | - |