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dc.contributor.authorRana, Kunjan D
dc.contributor.authorKhan, Sheraz
dc.contributor.authorHämäläinen, Matti S.
dc.contributor.authorVaina, Lucia M.
dc.date.accessioned2020-07-22T16:43:47Z
dc.date.available2020-07-22T16:43:47Z
dc.date.issued2020-06-12
dc.identifier.issn1475-925X
dc.identifier.urihttps://hdl.handle.net/1721.1/126312
dc.description.abstractBACKGROUND: Neurofeedback aids volitional control of one’s own brain activity using non-invasive recordings of brain activity. The applications of neurofeedback include improvement of cognitive performance and treatment of various psychiatric and neurological disorders. During real-time magnetoencephalography (rt-MEG), sensor-level or source-localized brain activity is measured and transformed into a visual feedback cue to the subject. Recent real-time fMRI (rt-fMRI) neurofeedback studies have used pattern recognition techniques to decode and train a brain state to link brain activities and cognitive behaviors. Here, we utilize the real-time decoding technique similar to ones employed in rt-fMRI to analyze time-varying rt-MEG signals. RESULTS: We developed a novel rt-MEG method, state-based neurofeedback (sb-NFB), to decode a time-varying brain state, a state signal, from which timings are extracted for neurofeedback training. The approach is entirely data-driven: it uses sensor-level oscillatory activity to find relevant features that best separate the targeted brain states. In a psychophysical task of spatial attention switching, we trained five young, healthy subjects using the sb-NFB method to decrease the time necessary for switch spatial attention from one visual hemifield to the other (referred to as switch time). Training resulted in a decrease in switch time with training. We saw that the activity targeted by the training involved proportional changes in alpha and beta-band oscillations, in sensors at the occipital and parietal regions. We also found that the state signal that encodes whether subjects attend to the left or right visual field effectively switches consistently with the task. CONCLUSION: We demonstrated the use of the sb-NFB method when the subject learns to increase the speed of shifting covert spatial attention from one visual field to the other. The sb-NFB method can target timing features that would otherwise also include extraneous features such as visual detection and motor response in a simple reaction time task.en_US
dc.description.sponsorshipNational Science Foundation (Grant 1545668)en_US
dc.description.sponsorshipNational Institute for Biomedical Imaging and Bioengineering (Grant 5U01EB023820)en_US
dc.description.sponsorshipNational Institute of Neurological Disorders and Stroke (Grant 1R01NS104585)en_US
dc.publisherBioMed Centralen_US
dc.relation.isversionof10.1186/s12938-020-00787-yen_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceBioMed Centralen_US
dc.titleA computational paradigm for real-time MEG neurofeedback for dynamic allocation of spatial attentionen_US
dc.typeArticleen_US
dc.identifier.citationRana, Kunjan D. et al. "A computational paradigm for real-time MEG neurofeedback for dynamic allocation of spatial attention." BioMedical Engineering OnLine 19 (June 2020): no. 45 doi 10.1186/s12938-020-00787-y ©2020 Author(s)en_US
dc.contributor.departmentMcGovern Institute for Brain Research at MITen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciencesen_US
dc.contributor.departmentMartinos Imaging Center (McGovern Institute for Brain Research at MIT)en_US
dc.relation.journalBioMedical Engineering OnLineen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2020-06-26T11:06:29Z
dc.language.rfc3066en
dc.rights.holderThe Author(s)
dspace.date.submission2020-06-26T11:06:29Z
mit.journal.volume19en_US
mit.licensePUBLISHER_CC


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