Using Deep learning to predict continuous hand kinematics from Magnetoencephalographic (MEG) measurements of electromagnetic brain activity

Detalles Bibliográficos
Autor: Anelli, Matteo
Tipo de recurso: tesis de maestría
Fecha de publicación:2020
País:España
Institución:Universidad Politécnica de Madrid
Repositorio:Archivo Digital UPM
OAI Identifier:oai:oa.upm.es:66689
Acceso en línea:https://oa.upm.es/66689/
Access Level:acceso abierto
Palabra clave:Magnetoencephalography
Convolutional Neural Network
Source Power comodulation
MEG
CNN
SPoC
Motor encoding
Sensorimotor rhythm
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repository_id_str
spelling Using Deep learning to predict continuous hand kinematics from Magnetoencephalographic (MEG) measurements of electromagnetic brain activityAnelli, MatteoMagnetoencephalographyConvolutional Neural NetworkSource Power comodulationMEGCNNSPoCMotor encodingSensorimotor rhythmParkkonen, LauriZubarev, Ivan20202020-12-31master thesishttp://purl.org/coar/resource_type/c_bdccinfo:eu-repo/semantics/masterThesishttps://oa.upm.es/66689/reponame:Archivo Digital UPMinstname:Universidad Politécnica de MadridInglésenopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:oa.upm.es:666892026-06-21T12:45:07Z
dc.title.none.fl_str_mv Using Deep learning to predict continuous hand kinematics from Magnetoencephalographic (MEG) measurements of electromagnetic brain activity
title Using Deep learning to predict continuous hand kinematics from Magnetoencephalographic (MEG) measurements of electromagnetic brain activity
spellingShingle Using Deep learning to predict continuous hand kinematics from Magnetoencephalographic (MEG) measurements of electromagnetic brain activity
Anelli, Matteo
Magnetoencephalography
Convolutional Neural Network
Source Power comodulation
MEG
CNN
SPoC
Motor encoding
Sensorimotor rhythm
title_short Using Deep learning to predict continuous hand kinematics from Magnetoencephalographic (MEG) measurements of electromagnetic brain activity
title_full Using Deep learning to predict continuous hand kinematics from Magnetoencephalographic (MEG) measurements of electromagnetic brain activity
title_fullStr Using Deep learning to predict continuous hand kinematics from Magnetoencephalographic (MEG) measurements of electromagnetic brain activity
title_full_unstemmed Using Deep learning to predict continuous hand kinematics from Magnetoencephalographic (MEG) measurements of electromagnetic brain activity
title_sort Using Deep learning to predict continuous hand kinematics from Magnetoencephalographic (MEG) measurements of electromagnetic brain activity
dc.creator.none.fl_str_mv Anelli, Matteo
author Anelli, Matteo
author_facet Anelli, Matteo
author_role author
dc.contributor.none.fl_str_mv Parkkonen, Lauri
Zubarev, Ivan
dc.subject.none.fl_str_mv Magnetoencephalography
Convolutional Neural Network
Source Power comodulation
MEG
CNN
SPoC
Motor encoding
Sensorimotor rhythm
topic Magnetoencephalography
Convolutional Neural Network
Source Power comodulation
MEG
CNN
SPoC
Motor encoding
Sensorimotor rhythm
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-12-31
dc.type.none.fl_str_mv master thesis
http://purl.org/coar/resource_type/c_bdcc
dc.type.openaire.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://oa.upm.es/66689/
url https://oa.upm.es/66689/
dc.language.none.fl_str_mv Inglés
en
language_invalid_str_mv Inglés
en
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Archivo Digital UPM
instname:Universidad Politécnica de Madrid
instname_str Universidad Politécnica de Madrid
reponame_str Archivo Digital UPM
collection Archivo Digital UPM
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869413110946725888
score 15,300719

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