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  Multiscale temporal neural dynamics predict performance in a complex sensorimotor task

Samek, W., Blythe, D. A. J., Curio, G., Müller, K.-R., Blankertz, B., & Nikulin, V. V. (2016). Multiscale temporal neural dynamics predict performance in a complex sensorimotor task. NeuroImage, 141, 291-303. doi:10.1016/j.neuroimage.2016.06.056.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002D-3C28-C Version Permalink: http://hdl.handle.net/21.11116/0000-0003-1C54-0
Genre: Journal Article

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 Creators:
Samek, Wojciech1, Author
Blythe, Duncan A. J.1, Author
Curio, Gabriel1, Author
Müller, Klaus-Robert1, Author
Blankertz, Benjamin1, Author
Nikulin, Vadim V.1, Author              
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1External Organizations, ou_persistent22              

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 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.

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Language(s): eng - English
 Dates: 2015-11-252016-06-302016-07-092016-11-01
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.neuroimage.2016.06.056
PMID: 27402598
Other: Epub 2016
 Degree: -

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Title: NeuroImage
Source Genre: Journal
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Publ. Info: Orlando, FL : Academic Press
Pages: - Volume / Issue: 141 Sequence Number: - Start / End Page: 291 - 303 Identifier: ISSN: 1053-8119
CoNE: https://pure.mpg.de/cone/journals/resource/954922650166