Decoding speech perception by native and non-native speakers using single-trial electrophysiological data.

Brain-computer interfaces (BCIs) are systems that use real-time analysis of neuroimaging data to determine the mental state of their user for purposes such as providing neurofeedback. Here, we investigate the feasibility of a BCI based on speech perception. Multivariate pattern classification method...

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Autores principales:	Alex Brandmeyer, Jason D R Farquhar, James M McQueen, Peter W M Desain
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4089d74e01ce4550a536418ec6385ec0
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spelling	oai:doaj.org-article:4089d74e01ce4550a536418ec6385ec02021-11-18T07:37:51ZDecoding speech perception by native and non-native speakers using single-trial electrophysiological data.1932-620310.1371/journal.pone.0068261https://doaj.org/article/4089d74e01ce4550a536418ec6385ec02013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23874567/?tool=EBIhttps://doaj.org/toc/1932-6203Brain-computer interfaces (BCIs) are systems that use real-time analysis of neuroimaging data to determine the mental state of their user for purposes such as providing neurofeedback. Here, we investigate the feasibility of a BCI based on speech perception. Multivariate pattern classification methods were applied to single-trial EEG data collected during speech perception by native and non-native speakers. Two principal questions were asked: 1) Can differences in the perceived categories of pairs of phonemes be decoded at the single-trial level? 2) Can these same categorical differences be decoded across participants, within or between native-language groups? Results indicated that classification performance progressively increased with respect to the categorical status (within, boundary or across) of the stimulus contrast, and was also influenced by the native language of individual participants. Classifier performance showed strong relationships with traditional event-related potential measures and behavioral responses. The results of the cross-participant analysis indicated an overall increase in average classifier performance when trained on data from all participants (native and non-native). A second cross-participant classifier trained only on data from native speakers led to an overall improvement in performance for native speakers, but a reduction in performance for non-native speakers. We also found that the native language of a given participant could be decoded on the basis of EEG data with accuracy above 80%. These results indicate that electrophysiological responses underlying speech perception can be decoded at the single-trial level, and that decoding performance systematically reflects graded changes in the responses related to the phonological status of the stimuli. This approach could be used in extensions of the BCI paradigm to support perceptual learning during second language acquisition.Alex BrandmeyerJason D R FarquharJames M McQueenPeter W M DesainPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 7, p e68261 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Alex Brandmeyer Jason D R Farquhar James M McQueen Peter W M Desain Decoding speech perception by native and non-native speakers using single-trial electrophysiological data.
description	Brain-computer interfaces (BCIs) are systems that use real-time analysis of neuroimaging data to determine the mental state of their user for purposes such as providing neurofeedback. Here, we investigate the feasibility of a BCI based on speech perception. Multivariate pattern classification methods were applied to single-trial EEG data collected during speech perception by native and non-native speakers. Two principal questions were asked: 1) Can differences in the perceived categories of pairs of phonemes be decoded at the single-trial level? 2) Can these same categorical differences be decoded across participants, within or between native-language groups? Results indicated that classification performance progressively increased with respect to the categorical status (within, boundary or across) of the stimulus contrast, and was also influenced by the native language of individual participants. Classifier performance showed strong relationships with traditional event-related potential measures and behavioral responses. The results of the cross-participant analysis indicated an overall increase in average classifier performance when trained on data from all participants (native and non-native). A second cross-participant classifier trained only on data from native speakers led to an overall improvement in performance for native speakers, but a reduction in performance for non-native speakers. We also found that the native language of a given participant could be decoded on the basis of EEG data with accuracy above 80%. These results indicate that electrophysiological responses underlying speech perception can be decoded at the single-trial level, and that decoding performance systematically reflects graded changes in the responses related to the phonological status of the stimuli. This approach could be used in extensions of the BCI paradigm to support perceptual learning during second language acquisition.
format	article
author	Alex Brandmeyer Jason D R Farquhar James M McQueen Peter W M Desain
author_facet	Alex Brandmeyer Jason D R Farquhar James M McQueen Peter W M Desain
author_sort	Alex Brandmeyer
title	Decoding speech perception by native and non-native speakers using single-trial electrophysiological data.
title_short	Decoding speech perception by native and non-native speakers using single-trial electrophysiological data.
title_full	Decoding speech perception by native and non-native speakers using single-trial electrophysiological data.
title_fullStr	Decoding speech perception by native and non-native speakers using single-trial electrophysiological data.
title_full_unstemmed	Decoding speech perception by native and non-native speakers using single-trial electrophysiological data.
title_sort	decoding speech perception by native and non-native speakers using single-trial electrophysiological data.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/4089d74e01ce4550a536418ec6385ec0
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Decoding speech perception by native and non-native speakers using single-trial electrophysiological data.

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