High Gamma-power Predicts Performance in Brain-computer Interfacing
نویسندگان
چکیده
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. High Gamma-Power Predicts Performance in Brain-Computer Interfacing Moritz Grosse-Wentrup & Bernhard Schölkopf Max Planck Institute for Intelligent Systems, Department Empirical Inference, Spemannstr. 38, 72076 Tübingen, Germany E-mail: [email protected], [email protected] 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. 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. High γ-Power Predicts BCI-Performance 2
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