High γ-power predicts performance in sensorimotor-rhythm brain-computer interfaces.
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.
Department Empirical Inference, Max Planck Institute for Intelligent Systems, Spemannstr. 38, 72076 Tübingen, Germany. moritzgw@ieee.org
Moritz
M
Grosse-Wentrup
Bernhard
B
Schölkopf
D058256:Brain Waves / Q000502:physiology*; D062207:Brain-Computer Interfaces; D005544:Forecasting; D006801:Humans; D007092:Imagination / Q000502:physiology*; D008297:Male; D010775:Photic Stimulation / Q000379:methods*; D011597:Psychomotor Performance / Q000502:physiology*