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A wireless brain-machine interface for real-time speech synthesis.

Frank H Guenther1, Jonathan S Brumberg, E Joseph Wright

  • 1Department of Cognitive and Neural Systems and Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, Massachusetts, United States of America. guenther@cns.bu.edu

Plos One
|December 17, 2009
PubMed
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This study demonstrates a new brain-machine interface (BMI) for speech restoration. Decoding attempted speech signals enabled a paralyzed individual to control a speech synthesizer, significantly improving accuracy with practice.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Speech Science

Background:

  • Brain-machine interfaces (BMIs) aim to restore function in paralyzed individuals.
  • Current BMIs offer slow communication rates through typing.
  • Novel approaches are needed for faster communication restoration.

Purpose of the Study:

  • To develop a novel speech restoration method using brain-machine interfaces.
  • To decode continuous auditory parameters from motor cortex activity.
  • To drive a real-time speech synthesizer for functional speech output.

Main Methods:

  • Implanted a Neurotrophic Electrode in the speech motor cortex of a volunteer with locked-in syndrome.
  • Used a Kalman filter-based decoder to translate neural signals into synthesizer parameters.

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  • Provided immediate auditory feedback of decoded speech sounds.

    • Main Results:

    • Achieved real-time control of a speech synthesizer from neural activity.
    • Demonstrated rapid improvement in vowel production accuracy with practice (25% increase in hit rate).
    • Reduced endpoint error by 46% during a three-vowel task.

    Conclusions:

    • Results support the feasibility of neural prostheses for near-conversational synthetic speech.
    • This technology holds potential for individuals with severe speech motor impairments.
    • Provides insights into the function of speech motor cortical neurons.