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

Authors
  • Stefano Filho, Carlos A1, 2
  • Attux, Romis2, 3
  • Castellano, Gabriela1, 2
  • 1 Neurophysics group, "Gleb Wataghing" Institute of Physics, University of Campinas, Campinas, São Paulo, Brazil. , (Brazil)
  • 2 Brazilian Institute of Neuroscience and Neurotechnology, Brazil. , (Brazil)
  • 3 Department of Computer Engineering and Industrial Automation, School of Electrical and Computer Engineering, University of Campinas, Campinas, São Paulo, Brazil. , (Brazil)
Type
Published Article
Journal
PeerJ
Publisher
PeerJ
Publication Date
Jan 01, 2017
Volume
5
Identifiers
DOI: 10.7717/peerj.3983
PMID: 29134143
Source
Medline
Keywords
License
Unknown

Abstract

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 (p < 0.05) with values ranging from 0.4 to 0.9 between PSD variations and functional network alterations for some electrodes, prominently in the beta band. The PSD method performed better for data classification, with mean accuracies of (90 ± 8)% and (87 ± 7)% for the mu and beta band, respectively, versus (83 ± 8)% and (83 ± 7)% for the same bands for the graph method. Moreover, the number of features for the graph method was considerably larger. However, results for both methods were relatively close, and even overlapped when the uncertainties of the accuracy rates were considered. Further investigation regarding a careful exploration of other graph metrics may provide better alternatives.

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