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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
Summary
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.
- 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.
