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dc.contributor.author
Peterson, Victoria
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Merk, Timon
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Bush, Alan
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Nikulin, Vadim
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Kühn, Andrea A.
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Neumann, Wolf Julian
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Richardson, R. Mark
dc.date.available
2024-09-09T15:41:45Z
dc.date.issued
2023-01
dc.identifier.citation
Peterson, Victoria; Merk, Timon; Bush, Alan; Nikulin, Vadim; Kühn, Andrea A.; et al.; Movement decoding using spatio-spectral features of cortical and subcortical local field potentials; Academic Press Inc Elsevier Science; Experimental Neurology; 359; 1-2023; 1-10
dc.identifier.issn
0014-4886
dc.identifier.uri
http://hdl.handle.net/11336/243878
dc.description.abstract
The first commercially sensing enabled deep brain stimulation (DBS) devices for the treatment of movement disorders have recently become available. In the future, such devices could leverage machine learning based brain signal decoding strategies to individualize and adapt therapy in real-time. As multi-channel recordings become available, spatial information may provide an additional advantage for informing machine learning models. To investigate this concept, we compared decoding performances from single channels vs. spatial filtering techniques using intracerebral multitarget electrophysiology in Parkinson´s disease patients undergoing DBS implantation. We investigated the feasibility of spatial filtering in invasive neurophysiology and the putative utility of combined cortical ECoG and subthalamic local field potential signals for decoding grip-force, a well-defined and continuous motor readout. We found that adding spatial information to the model can improve decoding (6% gain in decoding), but the spatial patterns and additional benefit was highly individual. Beyond decoding performance results, spatial filters and patterns can be used to obtain meaningful neurophysiological information about the brain networks involved in target behavior. Our results highlight the importance of individualized approaches for brain signal decoding, for which multielectrode recordings and spatial filtering can improve precision medicine approaches for clinical brain computer interfaces.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Academic Press Inc Elsevier Science
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
ADAPTIVE DEEP BRAIN STIMULATION
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INVASIVE NEURAL OSCILLATION
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MACHINE LEARNING
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MOVEMENT DECODING
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MULTICHANNEL RECORDINGS
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SPATIAL FILTERS
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Otras Ciencias de la Computación e Información
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Ciencias de la Computación e Información
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CIENCIAS NATURALES Y EXACTAS
dc.title
Movement decoding using spatio-spectral features of cortical and subcortical local field potentials
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2024-04-17T13:06:31Z
dc.journal.volume
359
dc.journal.pagination
1-10
dc.journal.pais
Estados Unidos
dc.description.fil
Fil: Peterson, Victoria. Harvard Medical School; Estados Unidos. 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
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Fil: Merk, Timon. Universitätsmedizin Berlin; Alemania
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Fil: Bush, Alan. Harvard Medical School; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
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Fil: Nikulin, Vadim. Max Planck Institute for Human Cognitive and Brain Sciences; Alemania
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Fil: Kühn, Andrea A.. Universitätsmedizin Berlin; Alemania
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Fil: Neumann, Wolf Julian. Universitätsmedizin Berlin; Alemania
dc.description.fil
Fil: Richardson, R. Mark. Harvard Medical School; Estados Unidos
dc.journal.title
Experimental Neurology
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.expneurol.2022.114261
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