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High Gamma-Power Predicts Performance in Brain-Computer Interfacing

MPS-Authors
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Grosse-Wentrup,  M
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Schölkopf,  B
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Fulltext (public)

TR-IntSys-003.pdf
(Publisher version), 2MB

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Citation

Grosse-Wentrup, M., & Schölkopf, B.(2012). High Gamma-Power Predicts Performance in Brain-Computer Interfacing (3). Tübingen, Germany: Max-Planck-Institut für Intelligente Systeme.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-B842-1
Abstract
Subjects operating a brain-computer interface (BCI) based on sensorimotor rhythms exhibit large variations in performance over the course of an experimental session. Here, we show that high-frequency gamma-oscillations, originating in fronto-parietal networks, predict such variations on a trial-to-trial basis. We interpret this finding as empirical support for an in uence of attentional networks on BCI-performance via modulation of the sensorimotor rhythm.