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Volitional control of neural activity: implications for brain-computer interfaces.

Eberhard E Fetz1

  • 1Department of Physiology and Biophysics, Washington National Primate Research Center, University of Washington, Seattle, WA 98195-7290, USA. fetz@u.washington.edu

The Journal of Physiology
|January 20, 2007
PubMed
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Brain-computer interfaces (BCI) and brain-machine interfaces (BMI) require volitional control of neural activity. This review examines evidence for such control, highlighting methods and limitations for improved BCI/BMI performance.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Rehabilitation Technology

Background:

  • Brain-computer interfaces (BCI) and brain-machine interfaces (BMI) are crucial for restoring function.
  • Successful BCI/BMI operation relies heavily on the volitional control of neural signals.
  • Cortical neuron activity is a primary focus for achieving this control.

Purpose of the Study:

  • To review evidence supporting volitional control of neural activity for BCI/BMI applications.
  • To identify factors limiting control accuracy in current BCI/BMI systems.
  • To explore potential improvements for BCI/BMI performance, particularly with long-term implants.

Main Methods:

  • Review of existing literature on neural signal control.
  • Analysis of conventional behavioral experiments demonstrating neural modulation.

Related Experiment Videos

  • Examination of operant conditioning studies using biofeedback.
  • Evaluation of BCI/BMI studies controlling external devices.

    • Main Results:

    • Evidence confirms volitional modulation of neural activity during various cognitive and motor tasks.
    • Operant conditioning and direct BCI/BMI control studies provide more direct evidence of volitional control.
    • Limited practice time is a key factor affecting control accuracy in BCI/BMI.
    • Preliminary data suggests recurrent BCIs with long-term implants may overcome some limitations.

    Conclusions:

    • Volitional control of neural activity is achievable and fundamental for BCI/BMI success.
    • Addressing limitations like practice time through advanced BCI/BMI designs, such as long-term implanted recurrent systems, is promising.
    • Further research into neural control mechanisms and advanced BCI/BMI architectures is warranted.