Please use this identifier to cite or link to this item: https://repositorio.ufu.br/handle/123456789/29566
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dc.creatorMelo, Mariana Cardoso-
dc.date.accessioned2020-07-28T00:40:59Z-
dc.date.available2020-07-28T00:40:59Z-
dc.date.issued2020-06-30-
dc.identifier.citationMELO, Mariana Cardoso. Decoding cortical response during motor tasks using brain connectivity. 2020. 116 f. Tese (Doutorado em Engenharia Elétrica) - Universidade Federal de Uberlândia, Uberlândia, 2020. Disponível em: http://doi.org/10.14393/ufu.te.2020.3003.pt_BR
dc.identifier.urihttps://repositorio.ufu.br/handle/123456789/29566-
dc.description.abstractSensorimotor integration is defined as the capacity of the central nervous system to integrate different sources of stimuli and transform such inputs in motor actions. Traditional approaches to measure sensorimotor dynamics are based on sensorimotor rhythms detected by Electroencephalography (EEG), such as the Event-Related Desynchronization (ERD) and Event-Related Synchronization (ERS). However, it is still not clear what are the underlying cortical dynamics involved in voluntary movements, and there is a lack of understanding of the temporal flow patterns related to a task. Although the models for motor decoding have improved considerably over the past decade, Brain-Machine Interfaces (BMIs), such as those attempting to control upper-limb prostheses, are still far from the goal of reaching naturalistic and dexterous control like our natural limbs. In this thesis, a model using connectivity estimators on EEG signals is proposed, with the aim of mapping cortical dynamics involved in sensorimotor integration while performing motor tasks. Here, special focus is given to wrist movements, since they are extremely important for proper handling of objects and have not been adequately explored in current literature associated with neural and standard control models used for upper-limb prosthesis. After initial screening, Mutual Information (MI) was chosen as the connectivity strategy for the aforementioned task. To estimate the most important channels and connectivity pairs of MI, a preliminary analysis based on the differences between resting and execution was performed. After the selection, MIs were estimated at higher temporal resolution, and separated in alpha and beta bands, from which a set of features was extracted and used as input for a Support Vector Machine (SVM) classifier to estimate the motor tasks (wrist pronation and wrist supination). For validation, we also estimated motor tasks using a conventional method for sensorimotor analysis, extracting significant ERD components and classified the data using SVM. The results showed higher accuracies when using the proposed model in beta band and MI features (89.65%). Alpha band resulted in 73.68% accuracy. On the other hand, using ERD, the accuracy of the classifier was 60.73% and 62.49% for alpha and beta bands, respectively. We conclude that the proposed method using functional connectivity and a proper model for the selection of important pairs over specific frequency bands has better response in identifying wrist movements. This strategy could be potentially applied in BMIs that control prosthetic devices at various levels.pt_BR
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superiorpt_BR
dc.languageengpt_BR
dc.publisherUniversidade Federal de Uberlândiapt_BR
dc.rightsAcesso Abertopt_BR
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectBrain Connectivitypt_BR
dc.subjectMutual Informationpt_BR
dc.subjectMotor taskpt_BR
dc.subjectElectroencephalographypt_BR
dc.subjectEvent-Related Desynchronizationpt_BR
dc.subjectBrain-Machine Interfacespt_BR
dc.titleDecoding cortical response during motor tasks using brain connectivitypt_BR
dc.title.alternativeDecodificação da resposta cortical durante tarefas motoras usando conectividade do cérebropt_BR
dc.typeTesept_BR
dc.contributor.advisor1Soares, Alcimar Barbosa-
dc.contributor.referee1Andrade, Adriano de Oliveira-
dc.contributor.referee2Siqueira Junior, Ailton Luiz Dias-
dc.contributor.referee3Oliveira, Sérgio Ricardo de Jesus-
dc.contributor.referee4Bastos Filho, Teodiano Freire-
dc.creator.Latteshttp://lattes.cnpq.br/3091157617551340pt_BR
dc.description.degreenameTese (Doutorado)pt_BR
dc.description.resumoSensorimotor integration is defined as the capacity of the central nervous system to integrate different sources of stimuli and transform such inputs in motor actions. Traditional approaches to measure sensorimotor dynamics are based on sensorimotor rhythms detected by Electroencephalography (EEG), such as the Event-Related Desynchronization (ERD) and Event-Related Synchronization (ERS). However, it is still not clear what are the underlying cortical dynamics involved in voluntary movements, and there is a lack of understanding of the temporal flow patterns related to a task. Although the models for motor decoding have improved considerably over the past decade, Brain-Machine Interfaces (BMIs), such as those attempting to control upper-limb prostheses, are still far from the goal of reaching naturalistic and dexterous control like our natural limbs. In this thesis, a model using connectivity estimators on EEG signals is proposed, with the aim of mapping cortical dynamics involved in sensorimotor integration while performing motor tasks. Here, special focus is given to wrist movements, since they are extremely important for proper handling of objects and have not been adequately explored in current literature associated with neural and standard control models used for upper-limb prosthesis. After initial screening, Mutual Information (MI) was chosen as the connectivity strategy for the aforementioned task. To estimate the most important channels and connectivity pairs of MI, a preliminary analysis based on the differences between resting and execution was performed. After the selection, MIs were estimated at higher temporal resolution, and separated in alpha and beta bands, from which a set of features was extracted and used as input for a Support Vector Machine (SVM) classifier to estimate the motor tasks (wrist pronation and wrist supination). For validation, we also estimated motor tasks using a conventional method for sensorimotor analysis, extracting significant ERD components and classified the data using SVM. The results showed higher accuracies when using the proposed model in beta band and MI features (89.65%). Alpha band resulted in 73.68% accuracy. On the other hand, using ERD, the accuracy of the classifier was 60.73% and 62.49% for alpha and beta bands, respectively. We conclude that the proposed method using functional connectivity and a proper model for the selection of important pairs over specific frequency bands has better response in identifying wrist movements. This strategy could be potentially applied in BMIs that control prosthetic devices at various levels.pt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.programPrograma de Pós-graduação em Engenharia Elétricapt_BR
dc.sizeorduration116pt_BR
dc.subject.cnpqCNPQ::ENGENHARIAS::ENGENHARIA BIOMEDICA::BIOENGENHARIA::PROCESSAMENTO DE SINAIS BIOLOGICOSpt_BR
dc.identifier.doihttp://doi.org/10.14393/ufu.te.2020.3003pt_BR
dc.orcid.putcode77979967-
dc.crossref.doibatchid2d14ea66-d3e4-4659-a390-cace60c999ad-
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