Multiscale temporal neural dynamics predict performance in a complex sensorimotor task

Wojciech Samek, Duncan A J Blythe, Gabriel Curio, Klaus Muller, Benjamin Blankertz, Vadim V. Nikulin

Research output: Contribution to journalArticle

11 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)291-303
Number of pages13
JournalNeuroImage
Volume141
DOIs
Publication statusPublished - 2016 Nov 1

Fingerprint

Brain
Electroencephalography
Signal-To-Noise Ratio
Cues
Software

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

Cite this

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

Multiscale temporal neural dynamics predict performance in a complex sensorimotor task. / Samek, Wojciech; Blythe, Duncan A J; Curio, Gabriel; Muller, Klaus; Blankertz, Benjamin; Nikulin, Vadim V.

In: NeuroImage, Vol. 141, 01.11.2016, p. 291-303.

Research output: Contribution to journalArticle

Samek, W, Blythe, DAJ, Curio, G, Muller, K, Blankertz, B & Nikulin, VV 2016, 'Multiscale temporal neural dynamics predict performance in a complex sensorimotor task', NeuroImage, vol. 141, pp. 291-303. https://doi.org/10.1016/j.neuroimage.2016.06.056
Samek, Wojciech ; Blythe, Duncan A J ; Curio, Gabriel ; Muller, Klaus ; Blankertz, Benjamin ; Nikulin, Vadim V. / Multiscale temporal neural dynamics predict performance in a complex sensorimotor task. In: NeuroImage. 2016 ; Vol. 141. pp. 291-303.
@article{855a9d32f65c4384a7775db0b71ba0dd,
title = "Multiscale temporal neural dynamics predict performance in a complex sensorimotor task",
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.",
author = "Wojciech Samek and Blythe, {Duncan A J} and Gabriel Curio and Klaus Muller and Benjamin Blankertz and Nikulin, {Vadim V.}",
year = "2016",
month = "11",
day = "1",
doi = "10.1016/j.neuroimage.2016.06.056",
language = "English",
volume = "141",
pages = "291--303",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Academic Press Inc.",

}

TY - JOUR

T1 - Multiscale temporal neural dynamics predict performance in a complex sensorimotor task

AU - Samek, Wojciech

AU - Blythe, Duncan A J

AU - Curio, Gabriel

AU - Muller, Klaus

AU - Blankertz, Benjamin

AU - Nikulin, Vadim V.

PY - 2016/11/1

Y1 - 2016/11/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84982792291&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84982792291&partnerID=8YFLogxK

U2 - 10.1016/j.neuroimage.2016.06.056

DO - 10.1016/j.neuroimage.2016.06.056

M3 - Article

VL - 141

SP - 291

EP - 303

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

ER -