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High gamma-power predicts performance in sensorimotor-rhythm brain-computer interfaces

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83948

Grosse-Wentrup,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Grosse-Wentrup, M., & Schölkopf, B. (2012). High gamma-power predicts performance in sensorimotor-rhythm brain-computer interfaces. Journal of Neural Engineering, 9(4), 8 pages. doi:10.1088/1741-2560/9/4/046001.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-B760-5
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 γ-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.