EEG sensorimotor rhythms' variation and functional connectivity measures during motor imagery: linear relations and classification approaches

Hands motor imagery (MI) has been reported to alter synchronization patterns amongst neurons, yielding variations in the mu and beta bands' power spectral density (PSD) of the electroencephalography (EEG) signal. These alterations have been used in the field of brain-computer interfaces (BCI),...

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Bibliographic Details
Published in:PeerJ (San Francisco, CA) CA), 2017-11, Vol.5, p.e3983-e3983, Article e3983
Main Authors: Stefano Filho, Carlos A, Attux, Romis, Castellano, Gabriela
Format: Article
Language:English
Subjects:
Analysis
BCI
Biomedical engineering
Brain-computer interface
Classification
Computational Science
Computer engineering
EEG
Electrodes
Electroencephalography
Emulation
Fourier transforms
Hands
Human-Computer Interaction
Interfaces
Localization (Brain function)
Mental task performance
Motor imagery
Motor skills
Neural networks
Neurology
Neuroscience
Neurosciences
Physiological aspects
Rehabilitation
Sensorimotor system
Signal processing
Spectrum analysis
Studies
Synchronization
Variation
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Summary:Hands motor imagery (MI) has been reported to alter synchronization patterns amongst neurons, yielding variations in the mu and beta bands' power spectral density (PSD) of the electroencephalography (EEG) signal. These alterations have been used in the field of brain-computer interfaces (BCI), in an attempt to assign distinct MI tasks to commands of such a system. Recent studies have highlighted that information may be missing if knowledge about brain functional connectivity is not considered. In this work, we modeled the brain as a graph in which each EEG electrode represents a node. Our goal was to understand if there exists any linear correlation between variations in the synchronization patterns-that is, variations in the PSD of mu and beta bands-induced by MI and alterations in the corresponding functional networks. Moreover, we (1) explored the feasibility of using functional connectivity parameters as features for a classifier in the context of an MI-BCI; (2) investigated three different types of feature selection (FS) techniques; and (3) compared our approach to a more traditional method using the signal PSD as classifier inputs. Ten healthy subjects participated in this study. We observed significant correlations (  
ISSN:2167-8359
2167-8359
DOI:10.7717/peerj.3983