Skip to main content

2022 | OriginalPaper | Buchkapitel

Single-Trial Functional Connectivity Dynamics of Event-Related Desynchronization for Motor Imagery EEG-Based Brain-Computer Interfaces

verfasst von : P. G. Rodrigues, A. Fim-Neto, J. R. Sato, D. C. Soriano, S. J. Nasuto

Erschienen in: XXVII Brazilian Congress on Biomedical Engineering

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Functional connectivity (FC) analysis has been widely applied to the study of the brain functional organization under different conditions and pathologies providing compelling results. Recently, the investigation of FC in motor tasks has drawn the attention of researchers devoted to post-stroke rehabilitation and those seeking robust features for the design of brain-computer interfaces (BCIs). In particular, concerning this application, it is crucial to understand: (1) how motor imagery (MI) networks dynamics evolve over time; (2) how it can be suitably characterized by its topological quantifiers (graph metrics); (3) what is the discrimination capability of graph metrics for BCI purposes. This work aims to investigate the MI single-trial time-course of functional connectivity defined in terms of event-related desynchronization/synchronization (ERD/S) similarity. Both ERD/S and clustering coefficient (CC) underlying FC were used as features for characterizing rest, right-hand MI, and left-hand MI for 21 subjects. Our results showed that MI can be associated with a higher CC when compared to rest, while right- and left-hand MI present a similar CC time-course evolution. From the classification standpoint, ERD/S, CC and their combination provided moderate to substantial single electrode peak performances (in terms of Cohen’s kappa) for discriminating rest and movement, i.e. for identifying alpha rhythm suppression. Weak peak classification performances were achieved for these features for right- and left-hand discrimination, but the combination of FC-based features and ERD/S provided significantly better results, suggesting complementary information. These results illustrate the symmetrical nature of brain activity relative power dynamics, as reflected in dynamics of functional connectivity during single trial MI and motivate need for further exploration of such measures for BCI applications.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Literatur
2.
3.
Zurück zum Zitat Díez-Cirarda M, Strafella AP, Kim J et al (2018) Dynamic functional connectivity in Parkinson’s disease patients with mild cognitive impairment and normal cognition. NeuroImage Clin 17:847–855CrossRef Díez-Cirarda M, Strafella AP, Kim J et al (2018) Dynamic functional connectivity in Parkinson’s disease patients with mild cognitive impairment and normal cognition. NeuroImage Clin 17:847–855CrossRef
5.
Zurück zum Zitat dos Santos SA, Biazoli CE Jr et al (2014) Abnormal functional resting-state networks in ADHD: graph theory and pattern recognition analysis of fMRI data. Biomed Res Int 2014:1–10 dos Santos SA, Biazoli CE Jr et al (2014) Abnormal functional resting-state networks in ADHD: graph theory and pattern recognition analysis of fMRI data. Biomed Res Int 2014:1–10
7.
Zurück zum Zitat Mottaz A, Corbet T, Doganci N et al (2018) Modulating functional connectivity after stroke with neurofeedback: effect on motor deficits in a controlled cross-over study. NeuroImage Clin 20:336–346CrossRef Mottaz A, Corbet T, Doganci N et al (2018) Modulating functional connectivity after stroke with neurofeedback: effect on motor deficits in a controlled cross-over study. NeuroImage Clin 20:336–346CrossRef
8.
Zurück zum Zitat Daly I, Nasuto SJ, Warwick K (2012) Brain computer interface control via functional connectivity dynamics. Pattern Recogn 45:2123–2136CrossRef Daly I, Nasuto SJ, Warwick K (2012) Brain computer interface control via functional connectivity dynamics. Pattern Recogn 45:2123–2136CrossRef
11.
Zurück zum Zitat Uribe LFS, Stefano Filho CA, de Oliveira VA et al (2019) A correntropy-based classifier for motor imagery brain-computer interfaces. Biomed Phys Eng Express 5:065026CrossRef Uribe LFS, Stefano Filho CA, de Oliveira VA et al (2019) A correntropy-based classifier for motor imagery brain-computer interfaces. Biomed Phys Eng Express 5:065026CrossRef
14.
Zurück zum Zitat de Lange FP, Roelofs K, Toni I (2008) Motor imagery: A window into the mechanisms and alterations of the motor system. Cortex 44:494–506CrossRef de Lange FP, Roelofs K, Toni I (2008) Motor imagery: A window into the mechanisms and alterations of the motor system. Cortex 44:494–506CrossRef
19.
Zurück zum Zitat Walsh NE, Jones L, McCabe CS (2015) The mechanisms and actions of motor imagery within the clinical setting. Textbook of neuromodulation. Springer New York, New York, NY, pp 151–158CrossRef Walsh NE, Jones L, McCabe CS (2015) The mechanisms and actions of motor imagery within the clinical setting. Textbook of neuromodulation. Springer New York, New York, NY, pp 151–158CrossRef
21.
Zurück zum Zitat Gonuguntla V, Wang Y, Veluvolu KC (2013) Phase synchrony in subject-specific reactive band of EEG for classification of motor imagery tasks. In: 35th Annual international conference on IEEE engineering in medicine and biology society. IEEE, pp 2784–2787. https://doi.org/10.1109/EMBC.2013.6610118 Gonuguntla V, Wang Y, Veluvolu KC (2013) Phase synchrony in subject-specific reactive band of EEG for classification of motor imagery tasks. In: 35th Annual international conference on IEEE engineering in medicine and biology society. IEEE, pp 2784–2787. https://​doi.​org/​10.​1109/​EMBC.​2013.​6610118
23.
Zurück zum Zitat Stefano Filho CA, Attux R, Castellano G (2017) EEG sensorimotor rhythms’ variation and functional connectivity measures during motor imagery: linear relations and classification approaches. PeerJ 5:e3983CrossRef Stefano Filho CA, Attux R, Castellano G (2017) EEG sensorimotor rhythms’ variation and functional connectivity measures during motor imagery: linear relations and classification approaches. PeerJ 5:e3983CrossRef
24.
Zurück zum Zitat Wairagkar M, Hayashi Y, Nasuto SJ (2018) Exploration of neural correlates of movement intention based on characterisation of temporal dependencies in electroencephalography. PLoS ONE 13:1–23CrossRef Wairagkar M, Hayashi Y, Nasuto SJ (2018) Exploration of neural correlates of movement intention based on characterisation of temporal dependencies in electroencephalography. PLoS ONE 13:1–23CrossRef
27.
Zurück zum Zitat Cohen MX (2014) Analyzing neural time series data: theory and practice, 1 edn. The MIT Press Cohen MX (2014) Analyzing neural time series data: theory and practice, 1 edn. The MIT Press
28.
Zurück zum Zitat von Luxburg U (2007) A tutorial on spectral clustering von Luxburg U (2007) A tutorial on spectral clustering
29.
Zurück zum Zitat Mohanty R, Sethares WA et al (2020) Rethinking measures of functional connectivity via feature extraction. Sci Rep 10:1–17CrossRef Mohanty R, Sethares WA et al (2020) Rethinking measures of functional connectivity via feature extraction. Sci Rep 10:1–17CrossRef
31.
Zurück zum Zitat Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52:1059–1069CrossRef Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52:1059–1069CrossRef
35.
Zurück zum Zitat Kim YK, Park E, Lee A et al (2018) Changes in network connectivity during motor imagery and execution. PLoS ONE 13:e0190715CrossRef Kim YK, Park E, Lee A et al (2018) Changes in network connectivity during motor imagery and execution. PLoS ONE 13:e0190715CrossRef
36.
Zurück zum Zitat Chung YG, Kang JH, Kim S-P (2011) Analysis of correlated EEG activity during motor imagery for brain-computer interfaces. In 2011 11th International conference on control, automation and systems Chung YG, Kang JH, Kim S-P (2011) Analysis of correlated EEG activity during motor imagery for brain-computer interfaces. In 2011 11th International conference on control, automation and systems
38.
Zurück zum Zitat Rodrigues PG, Fim Neto A, Takahata AK et al (2019) Dynamic eigenvector centrality as a biomarker for motor imagery brain-computer interfaces. An do XII Simpósio Eng Biomédica–IX Simpósio Instrumentação e Imagens Médicas. https://doi.org/10.5281/zenodo.3461135 Rodrigues PG, Fim Neto A, Takahata AK et al (2019) Dynamic eigenvector centrality as a biomarker for motor imagery brain-computer interfaces. An do XII Simpósio Eng Biomédica–IX Simpósio Instrumentação e Imagens Médicas. https://​doi.​org/​10.​5281/​zenodo.​3461135
Metadaten
Titel
Single-Trial Functional Connectivity Dynamics of Event-Related Desynchronization for Motor Imagery EEG-Based Brain-Computer Interfaces
verfasst von
P. G. Rodrigues
A. Fim-Neto
J. R. Sato
D. C. Soriano
S. J. Nasuto
Copyright-Jahr
2022
DOI
https://doi.org/10.1007/978-3-030-70601-2_275

Neuer Inhalt