Multiscale temporal neural dynamics predict performance in a complex sensorimotor task

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dc.contributor.authorSamek, Wojciech
dc.contributor.authorBlythe, Duncan A. J.
dc.contributor.authorCurio, Gabriel
dc.contributor.authorKlaus-Robert, Müller
dc.contributor.authorBlankertz, Benjamin
dc.contributor.authorNikulin, Vadim V.
dc.date.accessioned2019-10-14T13:46:11Z
dc.date.available2019-10-14T13:46:11Z
dc.date.issued2016-07-09
dc.description.abstractOngoing neuronal oscillations are pivotal in brain functioning and are known to influence subjects' performance. This modulation is usually studied on short time scales whilst multiple time scales are rarely considered. In our study we show that Long-Range Temporal Correlations (LRTCs) estimated from the amplitude of EEG oscillations over a range of time-scales predict performance in a complex sensorimotor task, based on Brain-Computer Interfacing (BCI). Our paradigm involved eighty subjects generating covert motor responses to dynamically changing visual cues and thus controlling a computer program through the modulation of neuronal oscillations. The neuronal dynamics were estimated with multichannel EEG. Our results show that: (a) BCI task accuracy may be predicted on the basis of LRTCs measured during the preceding training session, and (b) this result was not due to signal-to-noise ratio of the ongoing neuronal oscillations. Our results provide direct empirical evidence in addition to previous theoretical work suggesting that scale-free neuronal dynamics are important for optimal brain functioning.en
dc.description.sponsorshipBMBF, 01GQ1115, Adaptive Gehirn-Computer-Schnittstellen (BCI) in nichtstationären Umgebungenen
dc.description.sponsorshipBMBF, 01IS14013A, Verbundprojekt: BBDC - Berliner Kompetenzzentrum für Big Dataen
dc.description.sponsorshipDFG, 103586207, GRK 1589: Verarbeitung sensorischer Informationen in neuronalen Systemenen
dc.description.sponsorshipBMBF, 01GQ1001C, Verbundprojekt: Bernstein Zentrum für Computational Neuroscience, Berlin - "Präzision und Variabilität" - Teilprojekt A1, A3, A5, A6, B1, B2, B4 und B6en
dc.identifier.eissn1095-9572
dc.identifier.issn1053-8119
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/10114
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-9102
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subject.ddc610 Medizin und Gesundheitde
dc.subject.otherLong-Range Temporal Correlationen
dc.subject.otherBrain-Computer-Interfaceen
dc.subject.otheraccuracyen
dc.subject.otherbrain functioningen
dc.subject.otherneuronal dynamicsen
dc.subject.otherLRTCen
dc.titleMultiscale temporal neural dynamics predict performance in a complex sensorimotor tasken
dc.typeArticleen
dc.type.versionacceptedVersionen
dcterms.bibliographicCitation.doi10.1016/j.neuroimage.2016.06.056en
dcterms.bibliographicCitation.journaltitleNeuroImageen
dcterms.bibliographicCitation.originalpublishernameElsevieren
dcterms.bibliographicCitation.originalpublisherplaceAmsterdamen
dcterms.bibliographicCitation.pageend303en
dcterms.bibliographicCitation.pagestart291en
dcterms.bibliographicCitation.volume141en
tub.accessrights.dnbfreeen
tub.affiliationFak. 4 Elektrotechnik und Informatik::Inst. Softwaretechnik und Theoretische Informatik::FG Neurotechnologiede
tub.affiliationFak. 4 Elektrotechnik und Informatik::Inst. Softwaretechnik und Theoretische Informatik::FG Maschinelles Lernende
tub.affiliation.facultyFak. 4 Elektrotechnik und Informatikde
tub.affiliation.facultyFak. 4 Elektrotechnik und Informatikde
tub.affiliation.groupFG Neurotechnologiede
tub.affiliation.groupFG Maschinelles Lernende
tub.affiliation.instituteInst. Softwaretechnik und Theoretische Informatikde
tub.affiliation.instituteInst. Softwaretechnik und Theoretische Informatikde
tub.publisher.universityorinstitutionTechnische Universität Berlinen

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