Neural Speech Decoding During Audition, Imagination and Production
| Autor: | A. Hema Murthy, Shrikanth S. Narayanan, Rini A. Sharon, Mriganka Sur |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Speech perception
General Computer Science speech-EEG correlation Computer science Speech recognition Electroencephalography brain computer interface 03 medical and health sciences 0302 clinical medicine medicine General Materials Science EEG Set (psychology) 030304 developmental biology 0303 health sciences Signal processing medicine.diagnostic_test imagined speech General Engineering unit classification Assistive technology lcsh:Electrical engineering. Electronics. Nuclear engineering lcsh:TK1-9971 030217 neurology & neurosurgery Decoding methods Neural decoding |
| Zdroj: | IEEE Access, Vol 8, Pp 149714-149729 (2020) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2020.3016756 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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