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Article: Recovery of correlated neuronal sources from EEG: The good and bad ways of using SOBI

TitleRecovery of correlated neuronal sources from EEG: The good and bad ways of using SOBI
Authors
KeywordsBlind source separation (BSS)
Issue Date2005
Citation
NeuroImage, 2005, v. 28, n. 2, p. 507-519 How to Cite?
AbstractSecond-order blind identification (SOBI) is a blind source separation (BSS) algorithm that has been applied to MEG and EEG data collected during a range of sensory, motor, and cognitive tasks. SOBI can decompose mixtures of electric or magnetic signals by utilizing detailed temporal structures present in the continuously recorded signals. Successful decomposition critically depends on the choice of temporal delay parameters used for computing multiple covariance matrices. Here, we present empirical findings from high-density EEG data (128 channels) to show that SOBI's ability to recover correlated neuronal sources critically depends on the appropriate use of these temporal delay parameters. Specifically, we applied SOBI to EEG data collected during correlated activation of the left and right primary somatosensory cortices (SI). We show that separation of signals originating from the left and right SI is better achieved by using a large number and a wide range of temporal delays between a few and several hundred milliseconds when compared to results using various subsets of these delays. The paper also offers non-mathematician/engineer users a gentle introduction to the inner workings of SOBI. © 2005 Elsevier Inc. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/228032
ISSN
2015 Impact Factor: 5.463
2015 SCImago Journal Rankings: 4.464

 

DC FieldValueLanguage
dc.contributor.authorTang, Akaysha C.-
dc.contributor.authorLiu, Jing Yu-
dc.contributor.authorSutherland, Matthew T.-
dc.date.accessioned2016-08-01T06:45:01Z-
dc.date.available2016-08-01T06:45:01Z-
dc.date.issued2005-
dc.identifier.citationNeuroImage, 2005, v. 28, n. 2, p. 507-519-
dc.identifier.issn1053-8119-
dc.identifier.urihttp://hdl.handle.net/10722/228032-
dc.description.abstractSecond-order blind identification (SOBI) is a blind source separation (BSS) algorithm that has been applied to MEG and EEG data collected during a range of sensory, motor, and cognitive tasks. SOBI can decompose mixtures of electric or magnetic signals by utilizing detailed temporal structures present in the continuously recorded signals. Successful decomposition critically depends on the choice of temporal delay parameters used for computing multiple covariance matrices. Here, we present empirical findings from high-density EEG data (128 channels) to show that SOBI's ability to recover correlated neuronal sources critically depends on the appropriate use of these temporal delay parameters. Specifically, we applied SOBI to EEG data collected during correlated activation of the left and right primary somatosensory cortices (SI). We show that separation of signals originating from the left and right SI is better achieved by using a large number and a wide range of temporal delays between a few and several hundred milliseconds when compared to results using various subsets of these delays. The paper also offers non-mathematician/engineer users a gentle introduction to the inner workings of SOBI. © 2005 Elsevier Inc. All rights reserved.-
dc.languageeng-
dc.relation.ispartofNeuroImage-
dc.subjectBlind source separation (BSS)-
dc.titleRecovery of correlated neuronal sources from EEG: The good and bad ways of using SOBI-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.neuroimage.2005.06.062-
dc.identifier.pmid16139528-
dc.identifier.scopuseid_2-s2.0-26644440169-
dc.identifier.volume28-
dc.identifier.issue2-
dc.identifier.spage507-
dc.identifier.epage519-

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