Group Differences in Time-Frequency Relevant Patterns for User-Independent BCI Applications

  • L. F. Velasquez-MartinezEmail author
  • F. Y. Zapata-Castaño
  • J. I. Padilla-Buritica
  • José Manuel Ferrández Vicente
  • G. Castellanos-Dominguez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11486)


We present a comparison of two known methodologies for group analysis in EEG signals, which are the analysis by Group ICA on synchronization and desynchronization ERS/ERD, and brain connectivity analysis by measuring wPLI, both analyzes based on the brain synchronization information. For comparison, we have taken into account different frequency bands related to sensorimotor stimuli and time segmentation in order to overcome the nonstationarity of the EEG signal. In addition, we have used a threshold algorithm to reduce the dimension of the connectivity matrix, conserving the connections that are most important for both methodologies. The results obtained from the BCI competition IV-2a database show that the variable can be measured between two different measurement spaces, using the Euclidean distance, conserving spatial zones with more meaningful physiological interpretation.


Event-related Synchronization/Desynchronization Functional connectivity Group analysis wPLI 



This work is supported by the project Programa de reconstrucción del tejido social en zonas de pos-conflicto en Colombia del proyecto Fortalecimiento docente desde la alfabetización mediática Informacional y la CTel, como estrategia didáctico-pedagógica y soporte para la recuperación de la confianza del tejido social afectado por el conflicto. Código SIGP 58950 Financiado por Fondo Nacional de Financiamiento para la Ciencia, la Tecnología y la Innovación, Fondo Francisco José de Caldas con contrato No 213-2018 con Código 58960.


  1. 1.
    Hardmeier, M., Hatz, F., Bousleiman, H., Schindler, C., Stam, C.J., Fuhr, P.: Reproducibility of functional connectivity and graph measures based on the phase lag index (PLI) and weighted phase lag index (wPLI) derived from high resolution EEG. PloS One 9(10), e108648 (2014)CrossRefGoogle Scholar
  2. 2.
    Allison, B.Z., Wolpaw, E.W., Wolpaw, J.R.: Brain-computer interface systems: progress and prospects. Expert Rev. Med. Dev. 4(4), 463–474 (2007)CrossRefGoogle Scholar
  3. 3.
    Huster, R., Plis, S.M., Calhoun, V.D.: Group-level component analyses of EEG: validation and evaluation. Front. Neurosci. 9, 254 (2015)CrossRefGoogle Scholar
  4. 4.
    Allen, E., Damaraju, E., Eichele, T., Wu, L., Calhoun, V.D.: EEG signatures of dynamic functional network connectivity states. Brain Topogr. 31(1), 101–116 (2018)CrossRefGoogle Scholar
  5. 5.
    Labounek, R., et al.: Stable scalp EEG spatiospectral patterns across paradigms estimated by group ICA. Brain Topogr. 31(1), 76–89 (2018)CrossRefGoogle Scholar
  6. 6.
    Zhang, Y., Zhou, G., Jin, J., Wang, X., Cichocki, A.: Optimizing spatial patterns with sparse filter bands for motor-imagery based brain-computer interface. J. Neurosci. Methods 255, 85–91 (2015)CrossRefGoogle Scholar
  7. 7.
    Dai, S., Wei, Q.: Electrode channel selection based on backtracking search optimization in motor imagery brain-computer interfaces. J. Integr. Neurosci. 16(3), 241–254 (2017)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Aviyente, S., Tootell, A., Bernat, E.M.: Time-frequency phase-synchrony approaches with ERPs. Int. J. Psychophysiol. 111, 88–97 (2017)CrossRefGoogle Scholar
  9. 9.
    Terrien, J., Germain, G., Marque, C., Karlsson, B.: Bivariate piecewise stationary segmentation; improved pre-treatment for synchronization measures used on non-stationary biological signals. Med. Eng. Phys. 35(8), 1188–1196 (2013)CrossRefGoogle Scholar
  10. 10.
    Reinhold Scherer, C.V.: Motor imagery based brain-computer interfaces, chap. 8, pp. 171 – 195 (2018)Google Scholar
  11. 11.
    Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-computer interfaces for communication and control. Clin. Neurophysiol. 113(6), 767–791 (2002)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • L. F. Velasquez-Martinez
    • 1
    Email author
  • F. Y. Zapata-Castaño
    • 1
  • J. I. Padilla-Buritica
    • 1
    • 2
  • José Manuel Ferrández Vicente
    • 2
  • G. Castellanos-Dominguez
    • 1
  1. 1.Signal Processing and Recognition GroupUniversidad Nacional de ColombiaManizalesColombia
  2. 2.Universidad Politécnica de CartagenaCartagenaSpain

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