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Main Title: Multiscale temporal neural dynamics predict performance in a complex sensorimotor task
Author(s): Samek, Wojciech
Blythe, Duncan A. J.
Curio, Gabriel
Klaus-Robert, Müller
Blankertz, Benjamin
Nikulin, Vadim V.
Type: Article
Language Code: en
Abstract: Ongoing 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.
Issue Date: 9-Jul-2016
Date Available: 14-Oct-2019
DDC Class: 610 Medizin und Gesundheit
Subject(s): Long-Range Temporal Correlation
brain functioning
neuronal dynamics
Sponsor/Funder: BMBF, 01GQ1115, Adaptive Gehirn-Computer-Schnittstellen (BCI) in nichtstationären Umgebungen
BMBF, 01IS14013A, Verbundprojekt: BBDC - Berliner Kompetenzzentrum für Big Data
DFG, 103586207, GRK 1589: Verarbeitung sensorischer Informationen in neuronalen Systemen
BMBF, 01GQ1001C, Verbundprojekt: Bernstein Zentrum für Computational Neuroscience, Berlin - "Präzision und Variabilität" - Teilprojekt A1, A3, A5, A6, B1, B2, B4 und B6
Journal Title: NeuroImage
Publisher: Elsevier
Publisher Place: Amsterdam
Volume: 141
Publisher DOI: 10.1016/j.neuroimage.2016.06.056
Page Start: 291
Page End: 303
EISSN: 1095-9572
ISSN: 1053-8119
Appears in Collections:FG Neurotechnologie » Publications
FG Maschinelles Lernen » Publications

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