Publicação: Decoding cortical response during motor tasks using brain connectivity
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CNPQ::ENGENHARIAS::ENGENHARIA BIOMEDICA::BIOENGENHARIA::PROCESSAMENTO DE SINAIS BIOLOGICOS
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Sensorimotor 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.
Abstract
Sensorimotor 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.
Palavras-chave
Brain Connectivity, Mutual Information, Motor task, Electroencephalography, Event-Related Desynchronization, Brain-Machine Interfaces
Citação
MELO, 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. DOI http://doi.org/10.14393/ufu.te.2020.3003.
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