Classification of intended phoneme production from chronic intracortical microelectrode recordings in speech motor cortex
We conducted a neurophysiological study of attempted speech production in a paralyzed human volunteer using chronic microelectrode recordings. The volunteer suffers from locked-in syndrome leaving him in a state of near-total paralysis, though he maintains good cognition and sensation. In this study...
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Frontiers Media S.A.
2011-05-01
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Series: | Frontiers in Neuroscience |
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Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnins.2011.00065/full |
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author | Jonathan S Brumberg E. Joe Wright Dinal S Andreasen Dinal S Andreasen Frank H Guenther Frank H Guenther Frank H Guenther Philip R Kennedy |
author_facet | Jonathan S Brumberg E. Joe Wright Dinal S Andreasen Dinal S Andreasen Frank H Guenther Frank H Guenther Frank H Guenther Philip R Kennedy |
author_sort | Jonathan S Brumberg |
collection | DOAJ |
description | We conducted a neurophysiological study of attempted speech production in a paralyzed human volunteer using chronic microelectrode recordings. The volunteer suffers from locked-in syndrome leaving him in a state of near-total paralysis, though he maintains good cognition and sensation. In this study, we investigated the feasibility of supervised classification techniques for prediction of intended phoneme production in the absence of any overt movements including speech. Such classification or decoding ability has the potential to greatly improve the quality-of-life of many people who are otherwise unable to speak by providing a direct communicative link to the general community. We examined the performance of three classifiers on a multi-class discrimination problem in which the items were 38 American English phonemes including monophthong and diphthong vowels and consonants. The three classifiers differed in performance, but averaged between 16-21% overall accuracy (chance-level is 1/38 or 2.6%). Further, the distribution of phonemes classified statistically above chance was non-uniform though 20 of 38 phonemes were classified with statistical significance for all three classifiers. These preliminary results suggest supervised classification techniques are capable of performing large scale multi-class discrimination for attempted speech production and may provide the basis for future communication prostheses. |
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issn | 1662-453X |
language | English |
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publishDate | 2011-05-01 |
publisher | Frontiers Media S.A. |
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series | Frontiers in Neuroscience |
spelling | doaj.art-1185856a3bf5442492cc35b13fdb91c22022-12-22T03:19:03ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2011-05-01510.3389/fnins.2011.000657880Classification of intended phoneme production from chronic intracortical microelectrode recordings in speech motor cortexJonathan S Brumberg0E. Joe Wright1Dinal S Andreasen2Dinal S Andreasen3Frank H Guenther4Frank H Guenther5Frank H Guenther6Philip R Kennedy7Boston UniversityNeural Signals, Inc.Neural Signals, Inc.Georgia Tech Research InstituteBoston UniversityBoston UniversityHarvard University-Massachusetts Institute of TechnologyNeural Signals, Inc.We conducted a neurophysiological study of attempted speech production in a paralyzed human volunteer using chronic microelectrode recordings. The volunteer suffers from locked-in syndrome leaving him in a state of near-total paralysis, though he maintains good cognition and sensation. In this study, we investigated the feasibility of supervised classification techniques for prediction of intended phoneme production in the absence of any overt movements including speech. Such classification or decoding ability has the potential to greatly improve the quality-of-life of many people who are otherwise unable to speak by providing a direct communicative link to the general community. We examined the performance of three classifiers on a multi-class discrimination problem in which the items were 38 American English phonemes including monophthong and diphthong vowels and consonants. The three classifiers differed in performance, but averaged between 16-21% overall accuracy (chance-level is 1/38 or 2.6%). Further, the distribution of phonemes classified statistically above chance was non-uniform though 20 of 38 phonemes were classified with statistical significance for all three classifiers. These preliminary results suggest supervised classification techniques are capable of performing large scale multi-class discrimination for attempted speech production and may provide the basis for future communication prostheses.http://journal.frontiersin.org/Journal/10.3389/fnins.2011.00065/fullMotor Cortexlocked-in syndromechronic recordingNeurotrophic Electrodespeech prosthesis |
spellingShingle | Jonathan S Brumberg E. Joe Wright Dinal S Andreasen Dinal S Andreasen Frank H Guenther Frank H Guenther Frank H Guenther Philip R Kennedy Classification of intended phoneme production from chronic intracortical microelectrode recordings in speech motor cortex Frontiers in Neuroscience Motor Cortex locked-in syndrome chronic recording Neurotrophic Electrode speech prosthesis |
title | Classification of intended phoneme production from chronic intracortical microelectrode recordings in speech motor cortex |
title_full | Classification of intended phoneme production from chronic intracortical microelectrode recordings in speech motor cortex |
title_fullStr | Classification of intended phoneme production from chronic intracortical microelectrode recordings in speech motor cortex |
title_full_unstemmed | Classification of intended phoneme production from chronic intracortical microelectrode recordings in speech motor cortex |
title_short | Classification of intended phoneme production from chronic intracortical microelectrode recordings in speech motor cortex |
title_sort | classification of intended phoneme production from chronic intracortical microelectrode recordings in speech motor cortex |
topic | Motor Cortex locked-in syndrome chronic recording Neurotrophic Electrode speech prosthesis |
url | http://journal.frontiersin.org/Journal/10.3389/fnins.2011.00065/full |
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