Abstract
MEG/EEG brain imaging approaches are commonly based on linear covariance matrices that contain the prior information needed to solve the inverse problem. We expect that non-linear covariance matrices (or kernel matrices) provide more information than the widely used smoothers (Loreta, MSP) or data-based matrices (beamformers). Data-based covariance matrices have shortcomings such as being prone to be singular, having limited capability in modeling, complicated relationships in the data, and having a fixed form of representation. The multiple sparse priors (MSP) algorithm provides flexibility but in its original form it only contains smoothers. In this work, we propose to modify both MSP and beamformers by introducing a Gaussian kernel matrix with the objective of enhancing the reconstruction of neural activity. The proposed approach was tested with two well-known simulation benchmarks: Haufe’s and SPM. Simulation results showed improvements in the ROIs recognition with Haufe’s benchmark, and smaller localization error with SPM benchmark. A real data validation (MEG and EEG) was performed with the faces-scrambled dataset. The expected active sources were obtained, but their strength presented slight variations.
This work was partially supported by COLCIENCIAS (research projects 122266140116 and 111974455497).
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References
Babadi, B., Obregon-Henao, G., Lamus, C., Hamalainen, M.: A subspace pursuit-based iterative greedy hierarchical solution to the neuromagnetic inverse problem. Neuroimage 87, 427–443 (2014)
Baillet, S., Mosher, J., Leahy, R.: Electromagnetic brain mapping. IEEE Signal Process. Mag. 18(6), 14–30 (2001)
Belanche, L.: Developments in kernel design. In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN, pp. 369–378 (2013)
Belardinelli, P., Ortiz, E., Barnes, G., Noppeney, U., Preissl, H.: Source reconstruction accuracy of MEG and EEG Bayesian inversion approaches. Plos One 7(12), e51985 (2012)
Dale, A.M., Sereno, M.: Improved localization of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: a linear approach. J. Cognit. Neurosci. 5, 162–176 (1993)
Engemann, D.A., Gramfort, A.: Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals. Neuroimage 108, 328–342 (2015)
Friston, K., Harrison, L., Daunizeau, J., Kiebel, S., Phillips, C., Trujillo-Barreto, N., Henson, R., Flandin, G., Mattout, J.: Multiple sparse priors for the M/EEG inverse problem. NeuroImage 39, 1104–1120 (2008)
Gartner, T.: A survey of kernels for structured data. ACM SIGKDD Explor. Newsl. 5(1), 49–58 (2003)
Grech, R., Cassar, T., Muscat, J., Camilleri, K., Fabri, S., Zervakis, M., Xanthopoulos, P., Sakkalis, V., Vanrumste, B.: Review on solving the inverse problem in EEG source analysis. J. Neuro Eng. Rehabil. 5(1), 25 (2008)
Haufe, S., Ewald, A.: A simulation framework for benchmarking EEG-based brain connectivity estimation methodologies. Brain Topogr. 2016, 1–18 (2016)
Henson, R., Mouchlianitis, E., Friston, K.: MEG and EEG data fusion: simultaneous localisation of face-evoked responses. NeuroImage 47(2), 581–589 (2009)
Henson, R.N., 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. Hum. Neurosci. 5, 16 (2011)
Huang, Y., Parra, L.C., Haufe, S.: The New York head- a precise standardized volume conductor model for EEG source localization and tES targeting. Neuroimage 140, 150–162 (2015)
Lin, F.H., Belliveau, J.W., Dale, A.M., Hamalainen, M.S.: Distributed current estimates using cortical orientation constraints. Hum. Brain Mapp. 29, 1–13 (2006)
López, J.D., Litvak, V., Espinosa, J., Friston, K., Barnes, G.: Algorithmic procedures for bayesian MEG/EEG source reconstruction in SPM. NeuroImage 84, 476–487 (2014)
Álvarez-Meza, A.M., Cárdenas-Peña, D., Castellanos-Dominguez, G.: Unsupervised kernel function building using maximization of information potential variability. In: Bayro-Corrochano, E., Hancock, E. (eds.) CIARP 2014. LNCS, vol. 8827, pp. 335–342. Springer, Cham (2014). doi:10.1007/978-3-319-12568-8_41
Pascual-Marqui, R.: Standardized low resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find. Exp. Clin. Pharmacol. 24, 5–24 (2002)
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)
Wahba, G.: Support Vector Machines, Reproducing Kernel Hilbert Spaces and the Randomized GACV. University of Winsconsin (1998)
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Duque-Muñoz, L., Martinez-Vargas, J.D., Castellanos-Dominguez, G., Vargas-Bonilla, J.F., López, J.D. (2017). Non-linear Covariance Estimation for Reconstructing Neural Activity with MEG/EEG Data. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Natural and Artificial Computation for Biomedicine and Neuroscience. IWINAC 2017. Lecture Notes in Computer Science(), vol 10337. Springer, Cham. https://doi.org/10.1007/978-3-319-59740-9_33
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