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Multidimensional control using a mobile-phone based brain-muscle-computer interface.

Scott Vernon1, Sanjay S Joshi

  • 1Electrical and Computer Engineering Graduate Group, University of California, Davis 95616, USA. stvernon41@gmail.com

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 19, 2012
PubMed
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This study introduces a novel brain-muscle-computer interface for severely paralyzed individuals. The device uses a mobile phone to translate muscle signals into commands, enabling environmental control through learned neuromuscular skills.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Rehabilitation Technology

Background:

  • Traditional brain-computer interfaces (BCIs) require direct brain signal measurement and rely on operant conditioning for control.
  • Brain-muscle-computer interfaces (BMCIs) offer an alternative by utilizing muscle signals, leveraging the brain's capacity for learning neuromuscular skills.

Purpose of the Study:

  • To develop and prototype a mobile-phone based BMCI for severely paralyzed individuals.
  • To explore the feasibility of using user-modulated power in two separate frequency bands of a single sEMG signal as simultaneous control channels.

Main Methods:

  • Recorded surface electromyographic (sEMG) activity from the Auricularis superior facial muscle using a standard electrode.
  • Processed the analog sEMG signal on an Android-based mobile phone.

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  • Utilized user-modulated power in two distinct frequency bands as two simultaneous control channels for machine control.
  • Sent commands to external devices via Bluetooth after signal processing.
  • Trained users through biofeedback using simple cursor-to-target activities on the phone screen.

    • Main Results:

    • Demonstrated a novel mobile-phone based BMCI prototype.
    • Showcased the ability of users to actively create specified power levels in two separate frequency bands of a single sEMG signal.
    • Established two separate and simultaneous control channels for machine control using these modulated frequency bands.

    Conclusions:

    • The developed BMCI offers a new avenue for assistive technology for severely paralyzed individuals.
    • The system effectively translates modulated muscle signals into device commands, enhancing environmental control capabilities.
    • This approach leverages the brain's learning ability for neuromuscular skills, offering a promising alternative to traditional BCIs.