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Rejuvenating Classical Source Localization Methods with Spatial Graph Filters

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Brain Informatics (BI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13974))

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Abstract

EEG/MEG source imaging (ESI) aims to find the underlying brain sources to explain the observed EEG or MEG measurement. Multiple classical approaches have been proposed to solve the ESI problem based on different neurophysiological assumptions. To support the clinical decision making, it is important to estimate not only the exact location of source signal but also the boundary of extended source activation. Traditional methods usually render over-diffuse or sparse solution, which limits the source extent estimation accuracy. In this work, we exploit the graph structure defined in the 3D mesh of the brain by decomposing the spatial graph signal into low-, medium-, and high-frequency sub-spaces, and leverage the low frequency components of graph Fourier basis to approximate the extended region of source activation. We integrate the classical source localization methods with the low frequency subspace components derived from the spatial graph signal. The proposed method can effectively reconstruct focal extent patterns and significantly improve the performance compared to classical algorithms through both synthetic data and real EEG data.

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References

  1. Wendel, K., et al.: EEG/MEG source imaging: methods, challenges, and open issues. Comput. Intell. Neurosci. 2009 (2009)

    Google Scholar 

  2. Michel, C.M., Brunet, D.: EEG source imaging: a practical review of the analysis steps. Front. Neurol. 10, 325 (2019)

    Article  Google Scholar 

  3. Huang, G., et al.: Electromagnetic source imaging via a data-synthesis-based convolutional encoder-decoder network. IEEE Trans. Neural Netw. Learn. Syst. (2022)

    Google Scholar 

  4. Hämäläinen, M.S., Ilmoniemi, R.J.: Interpreting magnetic fields of the brain: minimum norm estimates. Med. Biol. Eng. Comput. 32(1), 35–42 (1994). https://doi.org/10.1007/BF02512476

    Article  Google Scholar 

  5. Dale, A.M., et al.: Dynamic statistical parametric mapping: combining fMRI and MEG for high-resolution imaging of cortical activity. Neuron 26(1), 55–67 (2000)

    Article  Google Scholar 

  6. Pascual-Marqui, R.D., et al.: Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find. Exp. Clin. Pharmacol. 24(Suppl D), 5–12 (2002)

    Google Scholar 

  7. Uutela, K., Hämäläinen, M., Somersalo, E.: Visualization of magnetoencephalographic data using minimum current estimates. Neuroimage 10(2), 173–180 (1999)

    Article  Google Scholar 

  8. Rao, B.D., Kreutz-Delgado, K.: An affine scaling methodology for best basis selection. IEEE Trans. Sig. Process. 47(1), 187–200 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  9. Gorodnitsky, I.F., George, J.S., Rao, B.D.: Neuromagnetic source imaging with FOCUSS: a recursive weighted minimum norm algorithm. Electroencephalogr. Clin. Neurophysiol. 95(4), 231–251 (1995)

    Article  Google Scholar 

  10. Bore, J.C., et al.: Sparse EEG source localization using LAPPS: least absolute l-P \((0 < p < 1)\) penalized solution. IEEE Trans. Biomed. Eng. 66(7), 1927–1939 (2018)

    Article  Google Scholar 

  11. Babadi, B., Obregon-Henao, G., Lamus, C., Hämäläinen, M.S., Brown, E.N., Purdon, P.L.: A subspace pursuit-based iterative greedy hierarchical solution to the neuromagnetic inverse problem. Neuroimage 87, 427–443 (2014)

    Article  Google Scholar 

  12. Wipf, D., Nagarajan, S.: A unified Bayesian framework for MEG/EEG source imaging. Neuroimage 44(3), 947–966 (2009)

    Article  Google Scholar 

  13. Ding, L., He, B.: Sparse source imaging in electroencephalography with accurate field modeling. Hum. Brain Mapp. 29(9), 1053–1067 (2008)

    Article  Google Scholar 

  14. Sohrabpour, A., Ye, S., et al.: Noninvasive electromagnetic source imaging and granger causality analysis: an electrophysiological connectome (eConnectome) approach. IEEE Trans. Biomed. Eng. 63(12), 2474–2487 (2016)

    Article  Google Scholar 

  15. Qin, J., Liu, F., Wang, S., Rosenberger, J.: EEG source imaging based on spatial and temporal graph structures. In: International Conference on Image Processing Theory, Tools and Applications (2017)

    Google Scholar 

  16. Liu, F., Wang, L., Lou, Y., Li, R.-C., Purdon, P.L.: Probabilistic structure learning for EEG/MEG source imaging with hierarchical graph priors. IEEE Trans. Med. Imaging 40(1), 321–334 (2020)

    Article  Google Scholar 

  17. Baillet, S., Mosher, J.C., Leahy, R.M.: Electromagnetic brain mapping. IEEE Sig. Process. Mag. 18(6), 14–30 (2001)

    Article  Google Scholar 

  18. Cai, C., Diwakar, M., Chen, D., Sekihara, K., Nagarajan, S.S.: Robust empirical Bayesian reconstruction of distributed sources for electromagnetic brain imaging. IEEE Trans. Med. Imaging 39(3), 567–577 (2019)

    Article  Google Scholar 

  19. Becker, H., et al.: EEG extended source localization: tensor-based vs. conventional methods. Neuroimage 96, 143–157 (2014)

    Article  Google Scholar 

  20. Liu, F., Wan, G., Semenov, Y.R., Purdon, P.L.: Extended electrophysiological source imaging with spatial graph filters. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13431, pp. 99–109. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16431-6_10

    Chapter  Google Scholar 

  21. M. Jiao, et al.: A graph Fourier transform based bidirectional LSTM neural network for EEG source imaging. Front. Neurosci. 447 (2022)

    Google Scholar 

  22. Fischl, B.: FreeSurfer. Neuroimage 62(2), 774–781 (2012)

    Article  Google Scholar 

  23. Gramfort, A., et al.: MNE software for processing MEG and EEG data. Neuroimage 86, 446–460 (2014)

    Article  Google Scholar 

  24. Gramfort, A., Kowalski, M., Hämäläinen, M.: Mixed-norm estimates for the M/EEG inverse problem using accelerated gradient methods. Phys. Med. Biol. 57(7), 1937 (2012)

    Article  Google Scholar 

  25. Gramfort, A., Strohmeier, D., Haueisen, J., Hämäläinen, M.S., Kowalski, M.: Time-frequency mixed-norm estimates: sparse M/EEG imaging with non-stationary source activations. Neuroimage 70, 410–422 (2013)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, 07030, USA

    Shihao Yang, Meng Jiao, Daphne Kalkanis & Feng Liu

  2. MEG Center, Division of Neurology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Jing Xiang

  3. Department of Neurosurgery, Rutgers Robert Wood Johnson Medical School, Brunswick, NJ, USA

    Hai Sun

Authors
  1. Shihao Yang
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  2. Meng Jiao
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  3. Jing Xiang
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  4. Daphne Kalkanis
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  5. Hai Sun
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  6. Feng Liu
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Corresponding author

Correspondence to Feng Liu .

Editor information

Editors and Affiliations

  1. Stevens Institute of Technology, Hoboken, NJ, USA

    Feng Liu

  2. Lehigh University, Bethlehem, PA, USA

    Yu Zhang

  3. Maebashi Institute of Technology, Maebashi, Japan

    Hongzhi Kuai

  4. Boston University, Boston, MA, USA

    Emily P. Stephen

  5. Stevens Institute of Technology, Hoboken, NJ, USA

    Hongjun Wang

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Yang, S., Jiao, M., Xiang, J., Kalkanis, D., Sun, H., Liu, F. (2023). Rejuvenating Classical Source Localization Methods with Spatial Graph Filters. In: Liu, F., Zhang, Y., Kuai, H., Stephen, E.P., Wang, H. (eds) Brain Informatics. BI 2023. Lecture Notes in Computer Science(), vol 13974. Springer, Cham. https://doi.org/10.1007/978-3-031-43075-6_25

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  • DOI: https://doi.org/10.1007/978-3-031-43075-6_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43074-9

  • Online ISBN: 978-3-031-43075-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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