Abstract
In this study, we analyze how the forward model dependence on the study population influences the reconstruction of brain activity based on electroencephalographic (EEG) recordings. To this, we compare the source localization accuracy using generic and atlas-based head models, constructed with the Finite Difference Reciprocity method (FDRM). Additionally, we analyze the influence of including several tissues, as skull, scalp, gray matter, white matter, and cerebrospinal fluid. Comparison is carried out under a parametric empirical Bayesian (PEB) framework, that allows contrasting different forward modeling approaches using real data. Obtained results, based on event-related potentials (ERPs) of 31 subjects, show that the more realistic and more dependent on the study population the used head model, the better the ESI estimation.
E. Cuartas-Morales—This work was supported by Prog. Nal. de Formación de Investigadores Generación del Bicentenario, 2012, Conv 528, program Jóvenes Investigadores e Innovadores, 2015, Conv 706, and by the research project 111956933522 founded by COLCIENCIAS.
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References
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Cuartas-Morales, E., Céspedes-Villar, Y.R., Martínez-Vargas, J.D., Arteaga-Daza, L.F., Castellanos-Dominguez, C. (2017). Influence of Population Dependent Forward Models on Distributed EEG Source Reconstruction. 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_37
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DOI: https://doi.org/10.1007/978-3-319-59740-9_37
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