A Wireless Brain-Machine Interface for Real-Time Speech Synthesis

A Wireless Brain-Machine Interface for Real-Time Speech Synthesis

doi:10.1371/journal.pone.0008218.g001″]

Frank H. Guenther1,2*, Jonathan S. Brumberg1,3, E. Joseph Wright3, Alfonso Nieto-Castanon4, Jason A. Tourville1, Mikhail Panko1, Robert Law1, Steven A. Siebert3, Jess L. Bartels3, Dinal S. Andreasen3,5, Princewill Ehirim6, Hui Mao7, Philip R. Kennedy3

1 Department of Cognitive and Neural Systems and Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, Massachusetts, United States of America, 2 Division of Health Sciences and Technology, Harvard University-Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America, 3 Neural Signals Inc., Duluth, Georgia, United States of America, 4 StatsANC LLC, Buenos Aires, Argentina, 5 Georgia Tech Research Institute, Marietta, Georgia, United States of America, 6 Gwinnett Medical Center, Lawrenceville, Georgia, United States of America, 7 Emory Center for Systems Imaging, Emory University Hospital, Atlanta, Georgia, United States of America

Abstract
Brain-machine interfaces (BMIs) involving electrodes implanted into the human cerebral cortex have recently been developed in an attempt to restore function to profoundly paralyzed individuals. Current BMIs for restoring communication can provide important capabilities via a typing process, but unfortunately they are only capable of slow communication rates. In the current study we use a novel approach to speech restoration in which we decode continuous auditory parameters for a real-time speech synthesizer from neuronal activity in motor cortex during attempted speech.

Methodology/Principal Findings
Neural signals recorded by a Neurotrophic Electrode implanted in a speech-related region of the left precentral gyrus of a human volunteer suffering from locked-in syndrome, characterized by near-total paralysis with spared cognition, were transmitted wirelessly across the scalp and used to drive a speech synthesizer. A Kalman filter-based decoder translated the neural signals generated during attempted speech into continuous parameters for controlling a synthesizer that provided immediate (within 50 ms) auditory feedback of the decoded sound. Accuracy of the volunteer’s vowel productions with the synthesizer improved quickly with practice, with a 25% improvement in average hit rate (from 45% to 70%) and 46% decrease in average endpoint error from the first to the last block of a three-vowel task.

Conclusions/Significance
Our results support the feasibility of neural prostheses that may have the potential to provide near-conversational synthetic speech output for individuals with severely impaired speech motor control. They also provide an initial glimpse into the functional properties of neurons in speech motor cortical areas.

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