Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation

Autores
Merk, Timon; Peterson, Victoria; Köhler, Richard; Haufe, Stefan; Richardson, R. Mark; Neumann, Wolf Julian
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Sensing enabled implantable devices and next-generation neurotechnology allow real-time adjustments of invasive neuromodulation. The identification of symptom and disease-specific biomarkers in invasive brain signal recordings has inspired the idea of demand dependent adaptive deep brain stimulation (aDBS). Expanding the clinical utility of aDBS with machine learning may hold the potential for the next breakthrough in the therapeutic success of clinical brain computer interfaces. To this end, sophisticated machine learning algorithms optimized for decoding of brain states from neural time-series must be developed. To support this venture, this review summarizes the current state of machine learning studies for invasive neurophysiology. After a brief introduction to the machine learning terminology, the transformation of brain recordings into meaningful features for decoding of symptoms and behavior is described. Commonly used machine learning models are explained and analyzed from the perspective of utility for aDBS. This is followed by a critical review on good practices for training and testing to ensure conceptual and practical generalizability for real-time adaptation in clinical settings. Finally, first studies combining machine learning with aDBS are highlighted. This review takes a glimpse into the promising future of intelligent adaptive DBS (iDBS) and concludes by identifying four key ingredients on the road for successful clinical adoption: i) multidisciplinary research teams, ii) publicly available datasets, iii) open-source algorithmic solutions and iv) strong world-wide research collaborations.
Fil: Merk, Timon. Charité – Universitätsmedizin Berlin; Alemania
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: Köhler, Richard. Charité – Universitätsmedizin Berlin; Alemania
Fil: Haufe, Stefan. Charité – Universitätsmedizin Berlin; Alemania
Fil: Richardson, R. Mark. Harvard Medical School; Estados Unidos
Fil: Neumann, Wolf Julian. Charité – Universitätsmedizin Berlin; Alemania
Materia
ADAPTIVE DEEP BRAIN STIMULATION
BRAIN-COMPUTER INTERFACE
CLOSED-LOOP DBS
MOVEMENT DISORDERS
NEURAL DECODING
REAL-TIME CLASSIFICATION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/212352
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Machine learning based brain signal decoding for intelligent adaptive deep brain stimulationMerk, TimonPeterson, VictoriaKöhler, RichardHaufe, StefanRichardson, R. MarkNeumann, Wolf JulianADAPTIVE DEEP BRAIN STIMULATIONBRAIN-COMPUTER INTERFACECLOSED-LOOP DBSMOVEMENT DISORDERSNEURAL DECODINGREAL-TIME CLASSIFICATIONhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Sensing enabled implantable devices and next-generation neurotechnology allow real-time adjustments of invasive neuromodulation. The identification of symptom and disease-specific biomarkers in invasive brain signal recordings has inspired the idea of demand dependent adaptive deep brain stimulation (aDBS). Expanding the clinical utility of aDBS with machine learning may hold the potential for the next breakthrough in the therapeutic success of clinical brain computer interfaces. To this end, sophisticated machine learning algorithms optimized for decoding of brain states from neural time-series must be developed. To support this venture, this review summarizes the current state of machine learning studies for invasive neurophysiology. After a brief introduction to the machine learning terminology, the transformation of brain recordings into meaningful features for decoding of symptoms and behavior is described. Commonly used machine learning models are explained and analyzed from the perspective of utility for aDBS. This is followed by a critical review on good practices for training and testing to ensure conceptual and practical generalizability for real-time adaptation in clinical settings. Finally, first studies combining machine learning with aDBS are highlighted. This review takes a glimpse into the promising future of intelligent adaptive DBS (iDBS) and concludes by identifying four key ingredients on the road for successful clinical adoption: i) multidisciplinary research teams, ii) publicly available datasets, iii) open-source algorithmic solutions and iv) strong world-wide research collaborations.Fil: Merk, Timon. Charité – Universitätsmedizin Berlin; AlemaniaFil: 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: Köhler, Richard. Charité – Universitätsmedizin Berlin; AlemaniaFil: Haufe, Stefan. Charité – Universitätsmedizin Berlin; AlemaniaFil: Richardson, R. Mark. Harvard Medical School; Estados UnidosFil: Neumann, Wolf Julian. Charité – Universitätsmedizin Berlin; AlemaniaAcademic Press Inc Elsevier Science2022-05info: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/11336/212352Merk, Timon; Peterson, Victoria; Köhler, Richard; Haufe, Stefan; Richardson, R. Mark; et al.; Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation; Academic Press Inc Elsevier Science; Experimental Neurology; 351; 5-2022; 1-170014-4886CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0014488622000188info:eu-repo/semantics/altIdentifier/doi/10.1016/j.expneurol.2022.113993info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:14:07Zoai:ri.conicet.gov.ar:11336/212352instacron: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 13:14:07.467CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation
title Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation
spellingShingle Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation
Merk, Timon
ADAPTIVE DEEP BRAIN STIMULATION
BRAIN-COMPUTER INTERFACE
CLOSED-LOOP DBS
MOVEMENT DISORDERS
NEURAL DECODING
REAL-TIME CLASSIFICATION
title_short Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation
title_full Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation
title_fullStr Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation
title_full_unstemmed Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation
title_sort Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation
dc.creator.none.fl_str_mv Merk, Timon
Peterson, Victoria
Köhler, Richard
Haufe, Stefan
Richardson, R. Mark
Neumann, Wolf Julian
author Merk, Timon
author_facet Merk, Timon
Peterson, Victoria
Köhler, Richard
Haufe, Stefan
Richardson, R. Mark
Neumann, Wolf Julian
author_role author
author2 Peterson, Victoria
Köhler, Richard
Haufe, Stefan
Richardson, R. Mark
Neumann, Wolf Julian
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv ADAPTIVE DEEP BRAIN STIMULATION
BRAIN-COMPUTER INTERFACE
CLOSED-LOOP DBS
MOVEMENT DISORDERS
NEURAL DECODING
REAL-TIME CLASSIFICATION
topic ADAPTIVE DEEP BRAIN STIMULATION
BRAIN-COMPUTER INTERFACE
CLOSED-LOOP DBS
MOVEMENT DISORDERS
NEURAL DECODING
REAL-TIME CLASSIFICATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Sensing enabled implantable devices and next-generation neurotechnology allow real-time adjustments of invasive neuromodulation. The identification of symptom and disease-specific biomarkers in invasive brain signal recordings has inspired the idea of demand dependent adaptive deep brain stimulation (aDBS). Expanding the clinical utility of aDBS with machine learning may hold the potential for the next breakthrough in the therapeutic success of clinical brain computer interfaces. To this end, sophisticated machine learning algorithms optimized for decoding of brain states from neural time-series must be developed. To support this venture, this review summarizes the current state of machine learning studies for invasive neurophysiology. After a brief introduction to the machine learning terminology, the transformation of brain recordings into meaningful features for decoding of symptoms and behavior is described. Commonly used machine learning models are explained and analyzed from the perspective of utility for aDBS. This is followed by a critical review on good practices for training and testing to ensure conceptual and practical generalizability for real-time adaptation in clinical settings. Finally, first studies combining machine learning with aDBS are highlighted. This review takes a glimpse into the promising future of intelligent adaptive DBS (iDBS) and concludes by identifying four key ingredients on the road for successful clinical adoption: i) multidisciplinary research teams, ii) publicly available datasets, iii) open-source algorithmic solutions and iv) strong world-wide research collaborations.
Fil: Merk, Timon. Charité – Universitätsmedizin Berlin; Alemania
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: Köhler, Richard. Charité – Universitätsmedizin Berlin; Alemania
Fil: Haufe, Stefan. Charité – Universitätsmedizin Berlin; Alemania
Fil: Richardson, R. Mark. Harvard Medical School; Estados Unidos
Fil: Neumann, Wolf Julian. Charité – Universitätsmedizin Berlin; Alemania
description Sensing enabled implantable devices and next-generation neurotechnology allow real-time adjustments of invasive neuromodulation. The identification of symptom and disease-specific biomarkers in invasive brain signal recordings has inspired the idea of demand dependent adaptive deep brain stimulation (aDBS). Expanding the clinical utility of aDBS with machine learning may hold the potential for the next breakthrough in the therapeutic success of clinical brain computer interfaces. To this end, sophisticated machine learning algorithms optimized for decoding of brain states from neural time-series must be developed. To support this venture, this review summarizes the current state of machine learning studies for invasive neurophysiology. After a brief introduction to the machine learning terminology, the transformation of brain recordings into meaningful features for decoding of symptoms and behavior is described. Commonly used machine learning models are explained and analyzed from the perspective of utility for aDBS. This is followed by a critical review on good practices for training and testing to ensure conceptual and practical generalizability for real-time adaptation in clinical settings. Finally, first studies combining machine learning with aDBS are highlighted. This review takes a glimpse into the promising future of intelligent adaptive DBS (iDBS) and concludes by identifying four key ingredients on the road for successful clinical adoption: i) multidisciplinary research teams, ii) publicly available datasets, iii) open-source algorithmic solutions and iv) strong world-wide research collaborations.
publishDate 2022
dc.date.none.fl_str_mv 2022-05
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/11336/212352
Merk, Timon; Peterson, Victoria; Köhler, Richard; Haufe, Stefan; Richardson, R. Mark; et al.; Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation; Academic Press Inc Elsevier Science; Experimental Neurology; 351; 5-2022; 1-17
0014-4886
CONICET Digital
CONICET
url http://hdl.handle.net/11336/212352
identifier_str_mv Merk, Timon; Peterson, Victoria; Köhler, Richard; Haufe, Stefan; Richardson, R. Mark; et al.; Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation; Academic Press Inc Elsevier Science; Experimental Neurology; 351; 5-2022; 1-17
0014-4886
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://www.sciencedirect.com/science/article/pii/S0014488622000188
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.expneurol.2022.113993
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Academic Press Inc Elsevier Science
publisher.none.fl_str_mv Academic Press Inc Elsevier Science
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
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score 12.993085

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