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dc.contributor.authorVaina, Lucia M.en_US
dc.contributor.authorRana, Kunjanen_US
dc.contributor.authorHämäläinen, Mattien_US
dc.date.accessioned2019-02-26T20:09:17Z
dc.date.accessioned2020-05-05T14:46:31Z
dc.date.available2019-02-26T20:09:17Z
dc.date.available2020-05-05T14:46:31Z
dc.date.issued2018-04-29
dc.identifier.citationL.M. Vaina, Kunjan Rana, Matti Hämäläinen. 2018. "Improving the Nulling Beamformer using Subspace Suppression." Frontiers in Computational Neuroscience, Volume 12, pp. 1 - 9. https://doi.org/10.3389/fncom.2018.00035
dc.identifier.issn1662-5188
dc.identifier.urihttps://hdl.handle.net/2144/40564
dc.description.abstractMagnetoencephalography (MEG) captures the magnetic fields generated by neuronal current sources with sensors outside the head. In MEG analysis these current sources are estimated from the measured data to identify the locations and time courses of neural activity. Since there is no unique solution to this so-called inverse problem, multiple source estimation techniques have been developed. The nulling beamformer (NB), a modified form of the linearly constrained minimum variance (LCMV) beamformer, is specifically used in the process of inferring interregional interactions and is designed to eliminate shared signal contributions, or cross-talk, between regions of interest (ROIs) that would otherwise interfere with the connectivity analyses. The nulling beamformer applies the truncated singular value decomposition (TSVD) to remove small signal contributions from a ROI to the sensor signals. However, ROIs with strong crosstalk will have high separating power in the weaker components, which may be removed by the TSVD operation. To address this issue we propose a new method, the nulling beamformer with subspace suppression (NBSS). This method, controlled by a tuning parameter, reweights the singular values of the gain matrix mapping from source to sensor space such that components with high overlap are reduced. By doing so, we are able to measure signals between nearby source locations with limited cross-talk interference, allowing for reliable cortical connectivity analysis between them. In two simulations, we demonstrated that NBSS reduces cross-talk while retaining ROIs' signal power, and has higher separating power than both the minimum norm estimate (MNE) and the nulling beamformer without subspace suppression. We also showed that NBSS successfully localized the auditory M100 event-related field in primary auditory cortex, measured from a subject undergoing an auditory localizer task, and suppressed cross-talk in a nearby region in the superior temporal sulcus.en_US
dc.format.extentp. 1 - 9en_US
dc.language.isoen_US
dc.publisherFrontiers Mediaen_US
dc.relation.ispartofFrontiers in Computational Neuroscience
dc.relation.replaceshttps://hdl.handle.net/2144/33628
dc.relation.replaces2144/33628
dc.rightsCopyright © 2018 Rana, Hämäläinen and Vaina. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectScience & technologyen_US
dc.subjectLife sciences & biomedicineen_US
dc.subjectMathematical & computational biologyen_US
dc.subjectNeurosciencesen_US
dc.subjectNeurosciences & neurologyen_US
dc.subjectMagnetoencephalographyen_US
dc.subjectSource localizationen_US
dc.subjectRegion of interesten_US
dc.subjectBeamformeren_US
dc.subjectSignal to noise ratioen_US
dc.subjectNulling beamformeren_US
dc.subjectCross-talken_US
dc.subjectMEGen_US
dc.subjectBrainen_US
dc.subjectClinical sciencesen_US
dc.titleImproving the nulling beamformer using subspace suppressionen_US
dc.typeArticleen_US
dc.description.versionAccepted manuscripten_US
dc.identifier.doi10.3389/fncom.2018.00035
pubs.elements-sourcemanual-entryen_US
pubs.notesArticle 35en_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Engineeringen_US
pubs.organisational-groupBoston University, College of Engineering, Department of Biomedical Engineeringen_US
pubs.publication-statusPublisheden_US
dc.identifier.orcid0000-0002-5636-8352 (Vaina, LM)
dc.identifier.mycv365162


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Copyright © 2018 Rana, Hämäläinen and Vaina. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Except where otherwise noted, this item's license is described as Copyright © 2018 Rana, Hämäläinen and Vaina. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.