Advertisement

Assessment of Source Connectivity for Emotional States Discrimination

  • J. D. Martinez-Vargas
  • D. A. Nieto-Mora
  • P. A. Muñoz-Gutiérrez
  • Y. R. Cespedes-Villar
  • E. Giraldo
  • G. Castellanos-Dominguez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11309)

Abstract

In this paper a novel methodology for assessing source connectivity applied to emotional states discrimination is proposed. The method involves (i) designing the set of Regions-of-interest (ROIs) over the cortical surface, (ii) estimating the ROI time-courses using a dynamic inverse problem formulation, (iii) estimating the pairwise functional connectivity between ROIs, and (iv) feeding a Support Vector Machine Classifier with the estimated connectivity to discriminate between emotional states. The performance of the proposed methodology is evaluated over a real database where obtained results improve state-of-the-art methods that either compute connectivity between pairs of EEG channels or do not consider the non-stationary nature of the EEG data.

Keywords

EEG inverse problem Connectivity Emotional states discrimination Regions of interest 

Notes

Acknowledgments

This work was carried out under the funding of COLCIENCIAS. Research project: 111077757982: “Sistema de identificación de fuentes epileptogénicas basado en medidas de conectividad funcional usando registros electroencefalográficos e imágenes de resonancia magnética en pacientes con epilepsia refractaria: apoyo a la cirugía resectiva”.

References

  1. 1.
    Koppert, M., Kalitzin, S., Velis, D., da Silva, F.L., Viergever, M.: Dynamics of collective multi-stability in models of distributed neuronal systems. Int. J. Neural Syst. 24(2), 1430004 (2014)CrossRefGoogle Scholar
  2. 2.
    Lei, X., Wu, T., Valdes-Sosa, P.A.: Incorporating priors for EEG source imaging and connectivity analysis. Front. Neurosci. 9, 284 (2015)CrossRefGoogle Scholar
  3. 3.
    Schoffelen, J.M., Gross, J.: Source connectivity analysis with MEG and EEG. Hum. Brain Mapp. 30(6), 1857–1865 (2009)CrossRefGoogle Scholar
  4. 4.
    Hurtado-Rincón, J.V., Martínez-Vargas, J.D., Rojas-Jaramillo, S., Giraldo, E., Castellanos-Dominguez, G.: Identification of relevant inter-channel EEG connectivity patterns: a kernel-based supervised approach. In: Ascoli, G.A., Hawrylycz, M., Ali, H., Khazanchi, D., Shi, Y. (eds.) BIH 2016. LNCS (LNAI), vol. 9919, pp. 14–23. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-47103-7_2CrossRefGoogle Scholar
  5. 5.
    Gupta, R., Hur, Y.J., Lavie, N.: Distracted by pleasure: effects of positive versus negative valence on emotional capture under load. Emotion 16(3), 328 (2016)CrossRefGoogle Scholar
  6. 6.
    Chella, F., Pizzella, V., Zappasodi, F., Marzetti, L.: Impact of the reference choice on scalp eeg connectivity estimation. J. Neural Eng. 13(3), 036016 (2016)CrossRefGoogle Scholar
  7. 7.
    Lai, M., Demuru, M., Hillebrand, A., Fraschini, M.: A comparison between scalp-and source-reconstructed EEG networks. Sci. Rep. 8(1), 12269 (2018)CrossRefGoogle Scholar
  8. 8.
    Bastos, A.M., Schoffelen, J.M.: A tutorial review of functional connectivity analysis methods and their interpretational pitfalls. Front. Syst. Neurosci. 9, 175 (2016)CrossRefGoogle Scholar
  9. 9.
    Baillet, S., Mosher, J.C., Leahy, R.M.: Electromagnetic brain mapping. IEEE Signal Process. Mag. 18, 14–30 (2001)CrossRefGoogle Scholar
  10. 10.
    Haufe, S., et al.: Large-scale EEG/MEG source localization with spatial flexibility. NeuroImage 54(2), 851–859 (2011)CrossRefGoogle Scholar
  11. 11.
    Castaño-Candamil, S., Höhne, J., Martínez-Vargas, J.D., An, X.W., Castellanos-Domínguez, G., Haufe, S.: Solving the EEG inverse problem based on space-time-frequency structured sparsity constraints. NeuroImage 118, 598–612 (2015)CrossRefGoogle Scholar
  12. 12.
    Martinez-Vargas, J.D., Strobbe, G., Vonck, K., van Mierlo, P., Castellanos-Dominguez, G.: Improved localization of seizure onset zones using spatiotemporal constraints and time-varying source connectivity. Front. Neurosci. 11, 156 (2017)CrossRefGoogle Scholar
  13. 13.
    Chen, X., Lin, Q., Kim, S., Carbonell, J.G., Xing, E.P.: Smoothing proximal gradient method for general structured sparse regression. Ann. Appl. Stat. 6(2), 719–752 (2012)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Gupta, R., Falk, T.H., et al.: Relevance vector classifier decision fusion and EEG graph-theoretic features for automatic affective state characterization. Neurocomputing 174, 875–884 (2016)CrossRefGoogle Scholar
  15. 15.
    Srinivasan, R., Winter, W.R., Ding, J., Nunez, P.L.: EEG and MEG coherence: measures of functional connectivity at distinct spatial scales of neocortical dynamics. J. Neurosci. Methods 166(1), 41–52 (2007)CrossRefGoogle Scholar
  16. 16.
    Koelstra, S., et al.: Deap: a database for emotion analysis; using physiological signals. IEEE Trans. Affect. Comput. 3(1), 18–31 (2012)CrossRefGoogle Scholar
  17. 17.
    Haufe, S., Ewald, A.: A simulation framework for benchmarking EEG-based brain connectivity estimation methodologies. Brain Topogr., 1–18 (2016).  https://doi.org/10.1007/s10548-016-0498-y, ISSN 1573-6792
  18. 18.
    Huang, Y., Parra, L.C., et al.: The new york head- a precise standardized volume conductor model for EEG source localization and TES targeting. NeuroImage 140, 150–162 (2016)CrossRefGoogle Scholar
  19. 19.
    Friston, K., et al.: Multiple sparse priors for the M/EEG inverse problem. NeuroImage 39(3), 1104–1120 (2008)CrossRefGoogle Scholar
  20. 20.
    Padilla-Buritica, J.I., Martinez-Vargas, J.D., Castellanos-Dominguez, G.: Emotion discrimination using spatially compact regions of interest extracted from imaging EEG activity. Front. Comput. Neurosci. 10, 55 (2016)CrossRefGoogle Scholar
  21. 21.
    Hata, M., et al.: Functional connectivity assessed by resting state eeg correlates with cognitive decline of alzheimer’s disease-an eloreta study. Clin. Neurophysiol. 127(2), 1269–1278 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • J. D. Martinez-Vargas
    • 1
  • D. A. Nieto-Mora
    • 1
  • P. A. Muñoz-Gutiérrez
    • 2
  • Y. R. Cespedes-Villar
    • 3
  • E. Giraldo
    • 4
  • G. Castellanos-Dominguez
    • 5
  1. 1.Instituto Tecnológico MetropolitanoMedellínColombia
  2. 2.Universidad del QuindíoArmeniaColombia
  3. 3.Centro de Bioinformatica y Biologia Computacional de Colombia - BIOSManizalesColombia
  4. 4.Universidad Tecnológica de PereiraPereiraColombia
  5. 5.Signal Processing and Recognition GroupUniversidad Nacional de ColombiaSede ManizalesColombia

Personalised recommendations