Abstract
Objective. Recent research has characterized the anatomical and functional basis of speech perception in the human auditory cortex. These advances have made it possible to decode speech information from activity in brain regions like the superior temporal gyrus, but no published work has demonstrated this ability in real-time, which is necessary for neuroprosthetic brain–computer interfaces. Approach. Here, we introduce a real-time neural speech recognition (rtNSR) software package, which was used to classify spoken input from high-resolution electrocorticography signals in real-time. We tested the system with two human subjects implanted with electrode arrays over the lateral brain surface. Subjects listened to multiple repetitions of ten sentences, and rtNSR classified what was heard in real-time from neural activity patterns using direct sentence-level and HMM-based phoneme-level classification schemes. Main results. We observed single-trial sentence classification accuracies of or higher for each subject with less than 7 minutes of training data, demonstrating the ability of rtNSR to use cortical recordings to perform accurate real-time speech decoding in a limited vocabulary setting. Significance. Further development and testing of the package with different speech paradigms could influence the design of future speech neuroprosthetic applications.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.