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Related Experiment Video

Updated: May 26, 2026

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

Learning to control brain rhythms: making a brain-computer interface possible.

Jaime A Pineda1, David S Silverman, Andrey Vankov

  • 1Cognitive Science Department 0515, University of California, San Diego, La Jolla, CA 92093, USA. pineda@cogsci.ucsd.edu

IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
|August 6, 2003
PubMed
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This study shows that individuals can learn to control brain activity for assistive brain-computer interfaces (BCIs). Training subjects to produce similar mu rhythm activity in both brain hemispheres was easier and faster than controlling each hemisphere independently.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Brain-computer interfaces (BCIs) leverage electroencephalographic (EEG) rhythms for device control.
  • The sensorimotor mu rhythm is a key target for BCI development.
  • Effective BCIs require intuitive user training and robust signal manipulation.

Purpose of the Study:

  • To investigate user ability to manipulate sensorimotor mu rhythms for BCI control.
  • To assess learning curves for producing similar versus differential hemispheric mu activity.
  • To evaluate BCI feasibility using a gamified, interactive task.

Main Methods:

  • Four subjects underwent ~10 hours of training over five weeks.
  • Subjects learned to modulate 8-12 Hz mu oscillations in motor cortex.

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Last Updated: May 26, 2026

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Published on: March 10, 2011

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  • Control involved producing similar or differential mu activity for a 3D video game task.

    • Main Results:

    • A steep learning curve was observed for differential mu activity, followed by a plateau.
    • Similar mu activity was acquired and maintained more easily throughout training.
    • Subjects learned to control mu activity levels faster when targeting similar hemispheric activity.

    Conclusions:

    • Intentional BCIs based on binary control signals are feasible.
    • Controlling similar mu activity across hemispheres is learned more rapidly than independent hemispheric control.
    • BCI systems can be enhanced through engaging, interactive tasks that facilitate user learning.