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EEG Based Brain Mapping by Using Frequency-Spatio-Temporal Constraints

  • Pablo Andrés Muñoz-Gutiérrez
  • Juan David Martinez-Vargas
  • Sergio Garcia-Vega
  • Eduardo Giraldo
  • German Castellanos-Dominguez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11309)

Abstract

In this paper an improvement of the dynamic inverse problem solution is proposed by using constraints in the space-time-frequency domain. The method is based on multi-rate filter banks for frequency selection of the EEG signals and a cost function that includes spatial and temporal constraints. As a result, an iterative method which includes Frequency-Spatio-temporal constraints is proposed. The performance of the proposed method is evaluated by using simulated and real EEG signals. It can be concluded that the enhanced IRA-L1 method with the frequency-spatio-temporal stage improves the quality of the brain reconstruction performance in terms of the Wasserstein metric, in comparison with the other methods, for both simulated and real EEG signals.

Keywords

Dynamic inverse problem Frequency-Spatio-temporal constraints 

Notes

Acknowledgments

This work was carried out under the funding of the Departamento Administrativo Nacional de Ciencia, Tecnología e Innovación (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”.

This work is also part of the research project “Solución del problema inverso dinámico considerando restricciones espacio-temporales no homogéneas aplicado a la reconstrucción de la actividad cerebral” funded by the Universidad Tecnológica de Pereira under the code E6-17-2.

References

  1. 1.
    Giraldo, E., Martinez-Vargas, J., Castellanos-Dominguez, G.: Reconstruction of neural activity from EEG data using dynamic spatio-temporal constraints. Int. J. Neural Syst. 26(7), 16500261–165002615 (2016)Google Scholar
  2. 2.
    Behjat, H., Leonardi, N., Sörnmo, L., Ville, D.V.D.: Anatomically-adapted graph wavelets for improved group-level fMRI activation mapping. NeuroImage 123(Supplement C), 185–199 (2015)CrossRefGoogle Scholar
  3. 3.
    Liao, K., Zhu, M., Ding, L.: A new wavelet transform to sparsely represent cortical current densities for eeg/meg inverse problems. Comput. Methods Programs Biomed. 111(2), 376–388 (2013)CrossRefGoogle Scholar
  4. 4.
    Lina, J., Chowdhury, R., Lemay, E., Kobayashi, E., Grova, C.: Wavelet-based localization of oscillatory sources from magnetoencephalography data. IEEE Trans. Biomed. Eng. 61(8), 2350–2364 (2014)CrossRefGoogle Scholar
  5. 5.
    Gramfort, A., Strohmeier, D., Haueisen, J., Hämäläinen, M., Kowalski, M.: Time-frequency mixed-norm estimates: Sparse m/eeg imaging with non-stationary source activations. NeuroImage 70, 410–422 (2013)CrossRefGoogle Scholar
  6. 6.
    Castaño-Candamil, S., et al.: Solving the eeg inverse problem based on space-time-frequency structured sparsity constraints. NeuroImage 118, 598–612 (2015)CrossRefGoogle Scholar
  7. 7.
    Wang, D., Miao, D., Xie, C.: Best basis-based wavelet packet entropy feature extraction and hierarchical eeg classification for epileptic detection. Expert. Syst. Appl. 38(11), 14314–14320 (2011)Google Scholar
  8. 8.
    Alickovic, E., Kevric, J., Subasi, A.: Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction. Biomed. Signal Process. Control 39(Supplement C), 94–102 (2018)CrossRefGoogle Scholar
  9. 9.
    Zhao, L., Jia, Y., Xie, Y.: Robust transcale decentralised estimation fusion for multisensor systems based on wavelet packet decomposition. IET Control Theory Appl. 8(8), 585–597 (2014)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Haufe, S., Ewald, A.: A simulation framework for benchmarking EEG-based brain connectivity estimation methodologies. Brain Topography, 1–18 (2016)Google Scholar
  11. 11.
    Henson, R., Wakeman, D.G., Litvak, V., Friston, K.J.: A parametric empirical bayesian framework for the EEG/MEG inverse problem: generative models for multi-subject and multi-modal integration. Front. Human Neurosci. 5(76), 1–16 (2011)Google Scholar
  12. 12.
    Friston, K., et al.: Multiple sparse priors for the M/EEG inverse problem. NeuroImage 39(3), 1104–1120 (2008)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Pablo Andrés Muñoz-Gutiérrez
    • 1
  • Juan David Martinez-Vargas
    • 2
  • Sergio Garcia-Vega
    • 4
  • Eduardo Giraldo
    • 3
  • German Castellanos-Dominguez
    • 4
  1. 1.Universidad del QuindíoArmeniaColombia
  2. 2.Instituto Tecnológico MetropolitanoMedellínColombia
  3. 3.Universidad Tecnológica de PereiraPereiraColombia
  4. 4.Signal Processing and Recognition GroupUniversidad Nacional de Colombia, sede ManizalesManizalesColombia

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