Show simple item record

dc.contributor.authorPinto-Orellana, Marco Antonio
dc.contributor.authorHammer, Hugo Lewi
dc.date.accessioned2021-01-31T10:42:01Z
dc.date.accessioned2021-03-08T15:18:04Z
dc.date.available2021-01-31T10:42:01Z
dc.date.available2021-03-08T15:18:04Z
dc.date.issued2020-08-18
dc.identifier.citationPinto-Orellana, Hammer. Analysis of Optical Brain Signals Using Connectivity Graph Networks. Lecture Notes in Computer Science (LNCS). 2020en
dc.identifier.isbn978-3-030-57320-1
dc.identifier.isbn978-3-030-57321-8
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.urihttps://hdl.handle.net/10642/9926
dc.description.abstractGraph network analysis (GNA) showed a remarkable role for understanding brain functions, but its application is mainly nar-rowed to fMRI research. Connectivity analysis (CA) is introduced as a signal-to-graph mapping in a time-causality framework. In this paper, we investigate the application of GNA/CA in fNIRS. To solve the inher-ent challenges of using CA, we also propose a novel metric: a maximum cross-lag magnitude (MCLM) that eÿciently extracts major causality in-formation. We tested MCLM in four types of cognitive activities (mental arithmetic, motor imagery, word generation, and brain workload) from 55 participants. CA/MCLM showed a compelling modeling capacity and re-vealed unexpected cross-subject network patterns. We found that motion imagery and mental arithmetic share a background network structure, and that the right prefrontal cortex, in AFp8, is an invariable destina-tion for information flows in every stimuli and participant. Therefore, CA/MCLM-fNIRS showed potential for its use along with fNIRS in clin-ical studies.en
dc.description.sponsorshipThis work is financially supported by the Research Council of Norway to the Project No. 273599, "Patient-Centric Engineering in Rehabilitation (PACER)".en
dc.language.isoenen
dc.publisherSpringeren
dc.relation.ispartofseriesLecture Notes in Computer Science;Volume 12279
dc.rightsThis is a post-peer-review, pre-copyedit version of a conference proceeding published in Machine Learning and Knowledge Extraction, 4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2020, Proceedings, which is part of the Lecture Notes in Computer Science book series (volume 12279). The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-57321-8_27en
dc.subjectBrain signalsen
dc.subjectFunctional near-infrared spectroscopyen
dc.subjectfNIRSen
dc.subjectGraph network analysesen
dc.subjectConnectivity analysesen
dc.titleAnalysis of Optical Brain Signals Using Connectivity Graph Networksen
dc.typeConference objecten
dc.typeJournal articleen
dc.typePeer revieweden
dc.date.updated2021-01-31T10:42:01Z
dc.description.versionacceptedVersionen
dc.identifier.cristin1827331
dc.source.journalLecture Notes in Computer Science (LNCS)
dc.relation.projectIDNorges forskningsråd: 273599


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record