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.
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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”.
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Martinez-Vargas, J.D., Nieto-Mora, D.A., Muñoz-Gutiérrez, P.A., Cespedes-Villar, Y.R., Giraldo, E., Castellanos-Dominguez, G. (2018). Assessment of Source Connectivity for Emotional States Discrimination. In: Wang, S., et al. Brain Informatics. BI 2018. Lecture Notes in Computer Science(), vol 11309. Springer, Cham. https://doi.org/10.1007/978-3-030-05587-5_7
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