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High Gamma-Power Predicts Performance in Brain-Computer Interfacing. MPI-IS-TR-003

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

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Schoelkopf,  Bernhard
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Citation

Grosse-Wentrup, M., & Schoelkopf, B.(2012). High Gamma-Power Predicts Performance in Brain-Computer Interfacing. MPI-IS-TR-003 (MPI-IS-TR-003). Tübingen: Max-Planck-Institut für Intelligente Systeme.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-4AE2-8
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 influence of attentional networks on BCI-performance via modulation of the sensorimotor rhythm.