Neural Speech Decoding During Audition, Imagination and Production
Name
09167421.pdf
Description
Published version
Size
4.49 MB
Format
Unknown
Checksum (MD5)
8dc1022873b56e6fa91c611c75d5465a
Author(s)
Sharon, Rini A
Narayanan, Shrikanth S
Sur, Mriganka
Murthy, A Hema
Date Issued
2020
Journal
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Version
Final published version
Abstract
© 2013 IEEE. Interpretation of neural signals to a form that is as intelligible as speech facilitates the development of communication mediums for the otherwise speech/motor-impaired individuals. Speech perception, production, and imagination often constitute phases of human communication. The primary goal of this article is to analyze the similarity between these three phases by studying electroencephalogram(EEG) patterns across these modalities, in order to establish their usefulness for brain computer interfaces. Neural decoding of speech using such non-invasive techniques necessitates the optimal choice of signal analysis and translation protocols. By employing selection-by-exclusion based temporal modeling algorithms, we discover fundamental syllable-like units that reveal similar set of signal signatures across all the three phases. Significantly higher than chance accuracies are recorded for single trial multi-unit EEG classification using machine learning approaches over three datasets across 30 subjects. Repeatability and subject independence tests performed at every step of the analysis further strengthens the findings and holds promise for translating brain signals to speech non-invasively.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ACCESS.2020.3016756
