Top

Published in:

2022 | OriginalPaper | Chapter

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

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

Published in: XXVII Brazilian Congress on Biomedical Engineering

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

previous chapter Real-Time Detection of Myoelectric Hand Patterns for an Incomplete Spinal Cord Injured Subject

next chapter A Lightweight Model for Human Activity Recognition Based on Two-Level Classifier and Compact CNN Model

Friston KJ (2011) Functional and effective connectivity: a review. Brain Connect 1:13–36. https://doi.org/10.1089/brain.2011.0008CrossRef

Sporns O (2011) Networks of the Brain. The MIT PressMATH

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

Núñez P, Poza J, Gómez C et al (2018) Analysis of electroencephalographic dynamic functional connectivity in Alzheimer’s disease. IFMBE Proc 68:165–168. https://doi.org/10.1007/978-981-10-9038-7_30CrossRef

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

Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10:186–198. https://doi.org/10.1038/nrn2575CrossRef

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

Daly I, Nasuto SJ, Warwick K (2012) Brain computer interface control via functional connectivity dynamics. Pattern Recogn 45:2123–2136CrossRef

Stefano Filho CA, Attux R, Castellano G (2018) Can graph metrics be used for EEG-BCIs based on hand motor imagery? Biomed Signal Process Control 40:359–365. https://doi.org/10.1016/j.bspc.2017.09.026CrossRef

10.

Rodrigues PG, Filho CAS, Attux R et al (2019) Space-time recurrences for functional connectivity evaluation and feature extraction in motor imagery brain-computer interfaces. Med Biol Eng Comput. https://doi.org/10.1007/s11517-019-01989-w

11.

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

12.

Wolpaw JR (2004) Brain-computer interfaces (BCIs) for communication and control: a mini-review. Suppl Clin Neurophysiol. https://doi.org/10.1016/S1567-424X(09)70400-3

13.

Wolpaw JR, Birbaumer N, McFarland DJ et al (2002) Brain-computer interfaces for communication and control. Clin Neurophysiol 113:767–791. https://doi.org/10.1016/S1388-2457(02)00057-3CrossRef

14.

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

15.

van Dokkum LEH, Ward T, Laffont I (2015) Brain computer interfaces for neurorehabilitation-its current status as a rehabilitation strategy post-stroke. Ann Phys Rehabil Med 58:3–8. https://doi.org/10.1016/j.rehab.2014.09.016CrossRef

16.

Yuan H, He B (2014) Brain-computer interfaces using sensorimotor rhythms: current state and future perspectives. IEEE Trans Biomed Eng 61:1425–1435. https://doi.org/10.1109/TBME.2014.2312397CrossRef

17.

Pfurtscheller G, Lopes Da Silva FHH (1999) Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 110:1842–1857. https://doi.org/10.1016/S1388-2457(99)00141-8CrossRef

18.

Hanakawa T (2016) Organizing motor imageries. Neurosci Res 104:56–63. https://doi.org/10.1016/j.neures.2015.11.003CrossRef

19.

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

20.

Li F, Peng W, Jiang Y et al (2019) The dynamic brain networks of motor imagery: time-varying causality analysis of scalp EEG. Int J Neural Syst 29:1850016. https://doi.org/10.1142/S0129065718500168CrossRef

21.

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

22.

Hamedi M, Salleh SH, Samdin SB et al (2015) Motor imagery brain functional connectivity analysis via coherence. In 2015 IEEE International conference on signal image processing application, pp 269–273. https://doi.org/10.1109/ICSIPA.2015.7412202

23.

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.

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

25.

Cho H, Ahn M, Ahn S et al (2017) EEG datasets for motor imagery brain–computer interface. Gigascience 6:1–8. https://doi.org/10.1093/gigascience/gix034CrossRef

26.

Perrin F, Pernier J, Bertrand O et al (1989) Spherical splines for scalp potential and current density mapping. Electroencephalogr Clin Neurophysiol 72:184–187. https://doi.org/10.1016/0013-4694(89)90180-6CrossRef

27.

Cohen MX (2014) Analyzing neural time series data: theory and practice, 1 edn. The MIT Press

28.

von Luxburg U (2007) A tutorial on spectral clustering

29.

Mohanty R, Sethares WA et al (2020) Rethinking measures of functional connectivity via feature extraction. Sci Rep 10:1–17CrossRef

30.

Zhang R, Yao D, Valdés-Sosa PA et al (2015) Efficient resting-state EEG network facilitates motor imagery performance. J Neural Eng 12:066024. https://doi.org/10.1088/1741-2560/12/6/066024CrossRef

31.

Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52:1059–1069CrossRef

32.

Onnela JP, Saramäki J, Kertész J et al (2005) Intensity and coherence of motifs in weighted complex networks. Phys Rev E - Stat Nonlinear Soft Matter Phys 71:1–4. https://doi.org/10.1103/PhysRevE.71.065103CrossRef

33.

Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20:37–46. https://doi.org/10.1177/001316446002000104CrossRef

34.

Boenstrup M, Feldheim J, Heise K et al (2014) The control of complex finger movements by directional information flow between mesial frontocentral areas and the primary motor cortex. Eur J Neurosci 40:2888–2897. https://doi.org/10.1111/ejn.12657CrossRef

35.

Kim YK, Park E, Lee A et al (2018) Changes in network connectivity during motor imagery and execution. PLoS ONE 13:e0190715CrossRef

36.

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

37.

Brunner C, Scherer R, Graimann et al (2006) Online control of a brain-computer interface using phase synchronization. IEEE Trans Biomed Eng 53:2501–2506. https://doi.org/10.1109/TBME.2006.881775CrossRef

38.

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

39.

Xu K, Huang Y-Y, Duann J-R (2019) The sensitivity of single-trial mu-suppression detection for motor imagery performance as compared to motor execution and motor observation performance. Front Hum Neurosci 13:1–12. https://doi.org/10.3389/fnhum.2019.00302CrossRef

40.

da Silva Costa TB, Uribe LFS, Carvalho S et al (2020) Channel capacity in brain–computer interfaces. J Neural Eng 17:016060. https://doi.org/10.1088/1741-2552/ab6cb7CrossRef

Title: Single-Trial Functional Connectivity Dynamics of Event-Related Desynchronization for Motor Imagery EEG-Based Brain-Computer Interfaces
Authors: P. G. Rodrigues
A. Fim-Neto
J. R. Sato
D. C. Soriano
S. J. Nasuto
Publisher: Springer International Publishing
Book: XXVII Brazilian Congress on Biomedical Engineering
Print ISBN: 978-3-030-70600-5

Electronic ISBN: 978-3-030-70601-2

Copyright Year: 2022
DOI: https://doi.org/10.1007/978-3-030-70601-2_275