Decoding of Covert Vowel Articulation Using Electroencephalography Cortical Currents

With the goal of providing assistive technology for the communication impaired, we proposed electroencephalography (EEG) cortical currents as a new approach for EEG-based brain-computer interface spellers. EEG cortical currents were estimated with a variational Bayesian method that uses functional m...

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Main Authors:	Yoshimura, Natsue, Nishimoto, Atsushi, Belkacem, Abdelkader Nasreddine, Shin, Duk, Kambara, Hiroyuki, Hanakawa, Takashi, Koike, Yasuharu
Format:	Online
Language:	English
Published:	Frontiers Media S.A. 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4853397/

id	pubmed-4853397
recordtype	oai_dc
spelling	pubmed-48533972016-05-19 Decoding of Covert Vowel Articulation Using Electroencephalography Cortical Currents Yoshimura, Natsue Nishimoto, Atsushi Belkacem, Abdelkader Nasreddine Shin, Duk Kambara, Hiroyuki Hanakawa, Takashi Koike, Yasuharu Neuroscience With the goal of providing assistive technology for the communication impaired, we proposed electroencephalography (EEG) cortical currents as a new approach for EEG-based brain-computer interface spellers. EEG cortical currents were estimated with a variational Bayesian method that uses functional magnetic resonance imaging (fMRI) data as a hierarchical prior. EEG and fMRI data were recorded from ten healthy participants during covert articulation of Japanese vowels /a/ and /i/, as well as during a no-imagery control task. Applying a sparse logistic regression (SLR) method to classify the three tasks, mean classification accuracy using EEG cortical currents was significantly higher than that using EEG sensor signals and was also comparable to accuracies in previous studies using electrocorticography. SLR weight analysis revealed vertices of EEG cortical currents that were highly contributive to classification for each participant, and the vertices showed discriminative time series signals according to the three tasks. Furthermore, functional connectivity analysis focusing on the highly contributive vertices revealed positive and negative correlations among areas related to speech processing. As the same findings were not observed using EEG sensor signals, our results demonstrate the potential utility of EEG cortical currents not only for engineering purposes such as brain-computer interfaces but also for neuroscientific purposes such as the identification of neural signaling related to language processing. Frontiers Media S.A. 2016-05-03 /pmc/articles/PMC4853397/ /pubmed/27199638 http://dx.doi.org/10.3389/fnins.2016.00175 Text en Copyright © 2016 Yoshimura, Nishimoto, Belkacem, Shin, Kambara, Hanakawa and Koike. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Yoshimura, Natsue Nishimoto, Atsushi Belkacem, Abdelkader Nasreddine Shin, Duk Kambara, Hiroyuki Hanakawa, Takashi Koike, Yasuharu
spellingShingle	Yoshimura, Natsue Nishimoto, Atsushi Belkacem, Abdelkader Nasreddine Shin, Duk Kambara, Hiroyuki Hanakawa, Takashi Koike, Yasuharu Decoding of Covert Vowel Articulation Using Electroencephalography Cortical Currents
author_facet	Yoshimura, Natsue Nishimoto, Atsushi Belkacem, Abdelkader Nasreddine Shin, Duk Kambara, Hiroyuki Hanakawa, Takashi Koike, Yasuharu
author_sort	Yoshimura, Natsue
title	Decoding of Covert Vowel Articulation Using Electroencephalography Cortical Currents
title_short	Decoding of Covert Vowel Articulation Using Electroencephalography Cortical Currents
title_full	Decoding of Covert Vowel Articulation Using Electroencephalography Cortical Currents
title_fullStr	Decoding of Covert Vowel Articulation Using Electroencephalography Cortical Currents
title_full_unstemmed	Decoding of Covert Vowel Articulation Using Electroencephalography Cortical Currents
title_sort	decoding of covert vowel articulation using electroencephalography cortical currents
description	With the goal of providing assistive technology for the communication impaired, we proposed electroencephalography (EEG) cortical currents as a new approach for EEG-based brain-computer interface spellers. EEG cortical currents were estimated with a variational Bayesian method that uses functional magnetic resonance imaging (fMRI) data as a hierarchical prior. EEG and fMRI data were recorded from ten healthy participants during covert articulation of Japanese vowels /a/ and /i/, as well as during a no-imagery control task. Applying a sparse logistic regression (SLR) method to classify the three tasks, mean classification accuracy using EEG cortical currents was significantly higher than that using EEG sensor signals and was also comparable to accuracies in previous studies using electrocorticography. SLR weight analysis revealed vertices of EEG cortical currents that were highly contributive to classification for each participant, and the vertices showed discriminative time series signals according to the three tasks. Furthermore, functional connectivity analysis focusing on the highly contributive vertices revealed positive and negative correlations among areas related to speech processing. As the same findings were not observed using EEG sensor signals, our results demonstrate the potential utility of EEG cortical currents not only for engineering purposes such as brain-computer interfaces but also for neuroscientific purposes such as the identification of neural signaling related to language processing.
publisher	Frontiers Media S.A.
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4853397/
_version_	1613574150270484480

Decoding of Covert Vowel Articulation Using Electroencephalography Cortical Currents

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