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High γ-power predicts performance in sensorimotor-rhythm brain-computer interfaces.

Moritz Grosse-Wentrup¹, Bernhard Schölkopf

¹Department Empirical Inference, Max Planck Institute for Intelligent Systems, Spemannstr. 38, 72076 Tübingen, Germany. moritzgw@ieee.org

Journal of Neural Engineering

|June 21, 2012

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High-frequency gamma oscillations in fronto-parietal networks predict brain-computer interface (BCI) performance fluctuations. This suggests attentional networks modulate BCI success by influencing sensorimotor rhythms.

Area of Science:

Neuroscience
Cognitive Science
Biomedical Engineering

Background:

Brain-computer interfaces (BCI) using sensorimotor rhythms show variable performance.
Understanding performance variability is crucial for BCI development.

Purpose of the Study:

To identify neural predictors of performance fluctuations in sensorimotor rhythm-based BCIs.
To investigate the role of brain oscillations in BCI task success.

Main Methods:

Analysis of high-frequency gamma oscillations (γ-oscillations) in fronto-parietal networks.
Correlation of oscillation power with trial-to-trial BCI performance.

Main Results:

High-frequency γ-oscillations originating in fronto-parietal networks significantly predicted BCI performance variations.

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The predictive power of γ-oscillations suggests a link between attentional processes and BCI control.

Conclusions:

Attentional networks influence sensorimotor rhythm-based BCI performance.
Modulation of sensorimotor rhythms by attention is a key factor in BCI variability.