Uncovering cortical MEG responses to listened audiobook stories

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Koskinen, M.
dc.contributor.author Seppä, M.
dc.date.accessioned 2017-05-11T09:15:24Z
dc.date.available 2017-05-11T09:15:24Z
dc.date.issued 2014
dc.identifier.citation Koskinen , M & Seppä , M 2014 , ' Uncovering cortical MEG responses to listened audiobook stories ' NEUROIMAGE , vol 100 , pp. 263-270 . DOI: 10.1016/j.neuroimage.2014.06.018 en
dc.identifier.issn 1053-8119
dc.identifier.issn 1095-9572
dc.identifier.other PURE UUID: f3ad229b-b6b3-4a51-aeef-f568533e2cf4
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/uncovering-cortical-meg-responses-to-listened-audiobook-stories(f3ad229b-b6b3-4a51-aeef-f568533e2cf4).html
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/11703734/1_s2.0_S1053811914004959_main_1.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/25970
dc.description.abstract Naturalistic stimuli, such as normal speech and narratives, are opening up intriguing prospects in neuroscience, especially when merging neuroimaging with machine learning methodology. Here we propose a task-optimized spatial filtering strategy for uncovering individual magnetoencephalographic (MEG) responses to audiobook stories. Ten subjects listened to 1-h-long recording once, as well as to 48 repetitions of a 1-min-long speech passage. Employing response replicability as statistical validity and utilizing unsupervised learning methods, we trained spatial filters that were able to generalize over datasets of an individual. For this blind-signal-separation (BSS) task, we derived a version of multi-set similarity-constrained canonical correlation analysis (SimCCA) that theoretically provides maximal signal-to-noise ratio (SNR) in this setting. Irrespective of significant noise in unaveraged MEG traces, the method successfully uncovered feasible time courses up to ~ 120 Hz, with the most prominent signals below 20 Hz. Individual trial-to-trial correlations of such time courses reached the level of 0.55 (median 0.33 in the group) at ~ 0.5 Hz, with considerable variation between subjects. By this filtering, the SNR increased up to 20 times. In comparison, independent component analysis (ICA) or principal component analysis (PCA) did not improve SNR notably. The validity of the extracted brain signals was further assessed by inspecting their associations with the stimulus, as well as by mapping the contributing cortical signal sources. The results indicate that the proposed methodology effectively reduces noise in MEG recordings to that extent that brain responses can be seen to nonrecurring audiobook stories. The study paves the way for applications aiming at accurately modeling the stimulus–response-relationship by tackling the response variability, as well as for real-time monitoring of brain signals of individuals in naturalistic experimental conditions. en
dc.format.extent 263-270
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries NEUROIMAGE en
dc.relation.ispartofseries Volume 100 en
dc.rights openAccess en
dc.subject.other 114 Physical sciences en
dc.subject.other 221 Nanotechnology en
dc.subject.other 214 Mechanical engineering en
dc.subject.other 218 Environmental engineering en
dc.title Uncovering cortical MEG responses to listened audiobook stories en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department School services, SCI
dc.contributor.department O.V.Lounasmaa-laboratorio
dc.contributor.department Department of Neuroscience and Biomedical Engineering en
dc.subject.keyword Canonical correlation analysis (CCA)
dc.subject.keyword Forward modeling
dc.subject.keyword MEG
dc.subject.keyword Single-trial analysis
dc.subject.keyword Spatial filtering
dc.subject.keyword Wavelet transform
dc.subject.keyword 114 Physical sciences
dc.subject.keyword 221 Nanotechnology
dc.subject.keyword 214 Mechanical engineering
dc.subject.keyword 218 Environmental engineering
dc.identifier.urn URN:NBN:fi:aalto-201705114345
dc.identifier.doi 10.1016/j.neuroimage.2014.06.018
dc.type.version publishedVersion


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