Neural Speech Decoding During Audition, Imagination and Production
© 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 prima...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers (IEEE)
2021
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| Online Access: | https://hdl.handle.net/1721.1/135408 |
| _version_ | 1826194235845509120 |
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| author | Sharon, Rini A Narayanan, Shrikanth S Sur, Mriganka Murthy, A Hema |
| author_facet | Sharon, Rini A Narayanan, Shrikanth S Sur, Mriganka Murthy, A Hema |
| author_sort | Sharon, Rini A |
| collection | MIT |
| description | © 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. |
| first_indexed | 2024-09-23T09:53:00Z |
| format | Article |
| id | mit-1721.1/135408 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T09:53:00Z |
| publishDate | 2021 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1354082021-10-28T04:39:10Z Neural Speech Decoding During Audition, Imagination and Production Sharon, Rini A Narayanan, Shrikanth S Sur, Mriganka Murthy, A Hema © 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. 2021-10-27T20:23:21Z 2021-10-27T20:23:21Z 2020 2021-03-22T15:33:46Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/135408 en 10.1109/ACCESS.2020.3016756 IEEE Access Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) IEEE |
| spellingShingle | Sharon, Rini A Narayanan, Shrikanth S Sur, Mriganka Murthy, A Hema Neural Speech Decoding During Audition, Imagination and Production |
| title | Neural Speech Decoding During Audition, Imagination and Production |
| title_full | Neural Speech Decoding During Audition, Imagination and Production |
| title_fullStr | Neural Speech Decoding During Audition, Imagination and Production |
| title_full_unstemmed | Neural Speech Decoding During Audition, Imagination and Production |
| title_short | Neural Speech Decoding During Audition, Imagination and Production |
| title_sort | neural speech decoding during audition imagination and production |
| url | https://hdl.handle.net/1721.1/135408 |
| work_keys_str_mv | AT sharonrinia neuralspeechdecodingduringauditionimaginationandproduction AT narayananshrikanths neuralspeechdecodingduringauditionimaginationandproduction AT surmriganka neuralspeechdecodingduringauditionimaginationandproduction AT murthyahema neuralspeechdecodingduringauditionimaginationandproduction |