A supervised data-driven spatial filter denoising method for speech artifacts in intracranial electrophysiological recordings

Autores
Peterson, Victoria; Vissani, Matteo; Luo, Shiyu; Rabbani, Qinwan; Crone, Nathan E.; Bush, Alan; Richardson, R. Mark
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Neurosurgical procedures that enable direct brain recordings in awake patients offer unique opportunities to explore the neurophysiology of human speech. The scarcity of these opportunities and the altruism of participating patients compel us to apply the highest rigor to signal analysis. Intracranial electroencephalography (iEEG) signals recorded during overt speech can contain a speech artifact that tracks the fundamental frequency (F0) of the participant’s voice, involving the same high-gamma frequencies that are modulated during speech production and perception. To address this artifact, we developed a spatial-filtering approach to identify and remove acoustic-induced contaminations of the recorded signal. We found that traditional reference schemes jeopardized signal quality, whereas our data-driven method denoised the recordings while preserving underlying neural activity.
Fil: Peterson, Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina. Harvard Medical School; Estados Unidos
Fil: Vissani, Matteo. Harvard Medical School; Estados Unidos
Fil: Luo, Shiyu. Johns Hopkins University School of Medicine; Estados Unidos
Fil: Rabbani, Qinwan. University Johns Hopkins; Estados Unidos
Fil: Crone, Nathan E.. Johns Hopkins University School of Medicine; Estados Unidos
Fil: Bush, Alan. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Harvard Medical School; Estados Unidos
Fil: Richardson, R. Mark. Harvard Medical School; Estados Unidos. Massachusetts Institute of Technology; Estados Unidos
Materia
SPEECH PRODUCTION
SPEECH ARTIFACT
iEEG
SPATIAL FILTERING
PHASE-COUPLING OPTIMIZATION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/258329
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A supervised data-driven spatial filter denoising method for speech artifacts in intracranial electrophysiological recordingsPeterson, VictoriaVissani, MatteoLuo, ShiyuRabbani, QinwanCrone, Nathan E.Bush, AlanRichardson, R. MarkSPEECH PRODUCTIONSPEECH ARTIFACTiEEGSPATIAL FILTERINGPHASE-COUPLING OPTIMIZATIONhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1https://purl.org/becyt/ford/2.6https://purl.org/becyt/ford/2https://purl.org/becyt/ford/1.7https://purl.org/becyt/ford/1Neurosurgical procedures that enable direct brain recordings in awake patients offer unique opportunities to explore the neurophysiology of human speech. The scarcity of these opportunities and the altruism of participating patients compel us to apply the highest rigor to signal analysis. Intracranial electroencephalography (iEEG) signals recorded during overt speech can contain a speech artifact that tracks the fundamental frequency (F0) of the participant’s voice, involving the same high-gamma frequencies that are modulated during speech production and perception. To address this artifact, we developed a spatial-filtering approach to identify and remove acoustic-induced contaminations of the recorded signal. We found that traditional reference schemes jeopardized signal quality, whereas our data-driven method denoised the recordings while preserving underlying neural activity.Fil: Peterson, Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina. Harvard Medical School; Estados UnidosFil: Vissani, Matteo. Harvard Medical School; Estados UnidosFil: Luo, Shiyu. Johns Hopkins University School of Medicine; Estados UnidosFil: Rabbani, Qinwan. University Johns Hopkins; Estados UnidosFil: Crone, Nathan E.. Johns Hopkins University School of Medicine; Estados UnidosFil: Bush, Alan. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Harvard Medical School; Estados UnidosFil: Richardson, R. Mark. Harvard Medical School; Estados Unidos. Massachusetts Institute of Technology; Estados UnidosMassachusetts Institute of Technology2024-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/258329Peterson, Victoria; Vissani, Matteo; Luo, Shiyu; Rabbani, Qinwan; Crone, Nathan E.; et al.; A supervised data-driven spatial filter denoising method for speech artifacts in intracranial electrophysiological recordings; Massachusetts Institute of Technology; Imaging Neuroscience; 2; 10-2024; 1-222837-6056CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://direct.mit.edu/imag/article/doi/10.1162/imag_a_00301/124344/A-supervised-data-driven-spatial-filter-denoisinginfo:eu-repo/semantics/altIdentifier/doi/10.1162/imag_a_00301info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:01:02Zoai:ri.conicet.gov.ar:11336/258329instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-10 13:01:03.007CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A supervised data-driven spatial filter denoising method for speech artifacts in intracranial electrophysiological recordings
title A supervised data-driven spatial filter denoising method for speech artifacts in intracranial electrophysiological recordings
spellingShingle A supervised data-driven spatial filter denoising method for speech artifacts in intracranial electrophysiological recordings
Peterson, Victoria
SPEECH PRODUCTION
SPEECH ARTIFACT
iEEG
SPATIAL FILTERING
PHASE-COUPLING OPTIMIZATION
title_short A supervised data-driven spatial filter denoising method for speech artifacts in intracranial electrophysiological recordings
title_full A supervised data-driven spatial filter denoising method for speech artifacts in intracranial electrophysiological recordings
title_fullStr A supervised data-driven spatial filter denoising method for speech artifacts in intracranial electrophysiological recordings
title_full_unstemmed A supervised data-driven spatial filter denoising method for speech artifacts in intracranial electrophysiological recordings
title_sort A supervised data-driven spatial filter denoising method for speech artifacts in intracranial electrophysiological recordings
dc.creator.none.fl_str_mv Peterson, Victoria
Vissani, Matteo
Luo, Shiyu
Rabbani, Qinwan
Crone, Nathan E.
Bush, Alan
Richardson, R. Mark
author Peterson, Victoria
author_facet Peterson, Victoria
Vissani, Matteo
Luo, Shiyu
Rabbani, Qinwan
Crone, Nathan E.
Bush, Alan
Richardson, R. Mark
author_role author
author2 Vissani, Matteo
Luo, Shiyu
Rabbani, Qinwan
Crone, Nathan E.
Bush, Alan
Richardson, R. Mark
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv SPEECH PRODUCTION
SPEECH ARTIFACT
iEEG
SPATIAL FILTERING
PHASE-COUPLING OPTIMIZATION
topic SPEECH PRODUCTION
SPEECH ARTIFACT
iEEG
SPATIAL FILTERING
PHASE-COUPLING OPTIMIZATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/2.6
https://purl.org/becyt/ford/2
https://purl.org/becyt/ford/1.7
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Neurosurgical procedures that enable direct brain recordings in awake patients offer unique opportunities to explore the neurophysiology of human speech. The scarcity of these opportunities and the altruism of participating patients compel us to apply the highest rigor to signal analysis. Intracranial electroencephalography (iEEG) signals recorded during overt speech can contain a speech artifact that tracks the fundamental frequency (F0) of the participant’s voice, involving the same high-gamma frequencies that are modulated during speech production and perception. To address this artifact, we developed a spatial-filtering approach to identify and remove acoustic-induced contaminations of the recorded signal. We found that traditional reference schemes jeopardized signal quality, whereas our data-driven method denoised the recordings while preserving underlying neural activity.
Fil: Peterson, Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina. Harvard Medical School; Estados Unidos
Fil: Vissani, Matteo. Harvard Medical School; Estados Unidos
Fil: Luo, Shiyu. Johns Hopkins University School of Medicine; Estados Unidos
Fil: Rabbani, Qinwan. University Johns Hopkins; Estados Unidos
Fil: Crone, Nathan E.. Johns Hopkins University School of Medicine; Estados Unidos
Fil: Bush, Alan. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Harvard Medical School; Estados Unidos
Fil: Richardson, R. Mark. Harvard Medical School; Estados Unidos. Massachusetts Institute of Technology; Estados Unidos
description Neurosurgical procedures that enable direct brain recordings in awake patients offer unique opportunities to explore the neurophysiology of human speech. The scarcity of these opportunities and the altruism of participating patients compel us to apply the highest rigor to signal analysis. Intracranial electroencephalography (iEEG) signals recorded during overt speech can contain a speech artifact that tracks the fundamental frequency (F0) of the participant’s voice, involving the same high-gamma frequencies that are modulated during speech production and perception. To address this artifact, we developed a spatial-filtering approach to identify and remove acoustic-induced contaminations of the recorded signal. We found that traditional reference schemes jeopardized signal quality, whereas our data-driven method denoised the recordings while preserving underlying neural activity.
publishDate 2024
dc.date.none.fl_str_mv 2024-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/258329
Peterson, Victoria; Vissani, Matteo; Luo, Shiyu; Rabbani, Qinwan; Crone, Nathan E.; et al.; A supervised data-driven spatial filter denoising method for speech artifacts in intracranial electrophysiological recordings; Massachusetts Institute of Technology; Imaging Neuroscience; 2; 10-2024; 1-22
2837-6056
CONICET Digital
CONICET
url http://hdl.handle.net/11336/258329
identifier_str_mv Peterson, Victoria; Vissani, Matteo; Luo, Shiyu; Rabbani, Qinwan; Crone, Nathan E.; et al.; A supervised data-driven spatial filter denoising method for speech artifacts in intracranial electrophysiological recordings; Massachusetts Institute of Technology; Imaging Neuroscience; 2; 10-2024; 1-22
2837-6056
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://direct.mit.edu/imag/article/doi/10.1162/imag_a_00301/124344/A-supervised-data-driven-spatial-filter-denoising
info:eu-repo/semantics/altIdentifier/doi/10.1162/imag_a_00301
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Massachusetts Institute of Technology
publisher.none.fl_str_mv Massachusetts Institute of Technology
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1842979922252922880
score 12.48226

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