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dc.contributor.authorZhigalov, A.
dc.contributor.authorHeinilä, Erkka
dc.contributor.authorParviainen, Tiina
dc.contributor.authorParkkonen, L.
dc.contributor.authorHyvärinen, A.
dc.date.accessioned2018-12-01T13:16:20Z
dc.date.available2020-01-16T22:35:41Z
dc.date.issued2019
dc.identifier.citationZhigalov, A., Heinilä, E., Parviainen, T., Parkkonen, L., & Hyvärinen, A. (2019). Decoding attentional states for neurofeedback : Mindfulness vs. wandering thoughts. <i>NeuroImage</i>, <i>185</i>, 565-574. <a href="https://doi.org/10.1016/j.neuroimage.2018.10.014" target="_blank">https://doi.org/10.1016/j.neuroimage.2018.10.014</a>
dc.identifier.otherCONVID_28667500
dc.identifier.otherTUTKAID_79185
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/60436
dc.description.abstractNeurofeedback requires a direct translation of neuronal brain activity to sensory input given to the user or subject. However, decoding certain states, e.g., mindfulness or wandering thoughts, from ongoing brain activity remains an unresolved problem. In this study, we used magnetoencephalography (MEG) to acquire brain activity during mindfulness meditation and thought-inducing tasks mimicking wandering thoughts. We used a novel real-time feature extraction to decode the mindfulness, i.e., to discriminate it from the thought-inducing tasks. The key methodological novelty of our approach is usage of MEG power spectra and functional connectivity of independent components as features underlying mindfulness states. Performance was measured as the classification accuracy on a separate session but within the same subject. We found that the spectral- and connectivity-based classification approaches allowed discriminating mindfulness and thought-inducing tasks with an accuracy around 60% compared to the 50% chance-level. Both classification approaches showed similar accuracy, although the connectivity approach slightly outperformed the spectral one in a few cases. Detailed analysis showed that the classification coefficients and the associated independent components were highly individual among subjects and a straightforward transfer of the coefficients over subjects provided near chance-level classification accuracy. Thus, discriminating between mindfulness and wandering thoughts seems to be possible, although with limited accuracy, by machine learning, especially on the subject-level. Our hope is that the developed spectral- and connectivity-based decoding methods can be utilized in real-time neurofeedback to decode mindfulness states from ongoing neuronal activity, and hence, provide a basis for improved, individualized mindfulness training.fi
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherElsevier Inc.
dc.relation.ispartofseriesNeuroImage
dc.rightsCC BY-NC-ND 4.0
dc.subject.otherneurofeedback
dc.subject.othermagnetoencephalography
dc.titleDecoding attentional states for neurofeedback : Mindfulness vs. wandering thoughts
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201811294932
dc.contributor.laitosPsykologian laitosfi
dc.contributor.laitosDepartment of Psychologyen
dc.contributor.oppiainePsykologiafi
dc.contributor.oppiaineMonitieteinen aivotutkimuskeskusfi
dc.contributor.oppiainePsychologyen
dc.contributor.oppiaineCentre for Interdisciplinary Brain Researchen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2018-11-29T10:15:08Z
dc.description.reviewstatuspeerReviewed
dc.format.pagerange565-574
dc.relation.issn1053-8119
dc.relation.numberinseries0
dc.relation.volume185
dc.type.versionacceptedVersion
dc.rights.copyright© 2018 Elsevier Inc.
dc.rights.accesslevelopenAccessfi
dc.subject.ysobiopalaute
dc.subject.ysokoneoppiminen
dc.subject.ysotietoinen läsnäolo
dc.subject.ysoMEG
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p25269
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p24919
jyx.subject.urihttp://www.yso.fi/onto/yso/p3329
dc.rights.urlhttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.relation.doi10.1016/j.neuroimage.2018.10.014
jyx.fundinginformationThis work was supported by the Academy of Finland [grant number295075] to AH, TP, LP; the Finnish Cultural Foundation [grant number00161132] to AZ.


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