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Abstract #5479

Fuzzy GLM approaches based on LR and alpha-cut representations for fMRI activity detection

Alejandro Veloz1,2, Steren Chabert1, Alejandro Weinstein1,3, Hector Allende2, and Claudio Moraga4

1Biomedical Engineering Department, Universidad de Valparaiso, Valparaiso, Chile, 2Informatics Department, Universidad Técnica Federico Santa María, Valparaiso, Chile, 3Advanced Center for Electrical and Electronic Engineering, Valparaiso, Chile, 4TU Dortmund University, Dortmund, Germany

The General Linear Model (GLM) approach is still the standard paradigm used in routine fMRI analysis. This method is based on a model of the BOLD response which depends on the Hemodynamic Response Function (HRF). The HRF ignores the intrinsic intra- and inter-subject variability, resulting in inaccuracies in the brain activity detection. This work leverages on fuzzy sets theory with the purpose of developing a fuzzy GLM to overcome limitations of current GLM-based approaches. We performed an evaluation on simulated and in vivo fMRI data. We compare our results with aproaches based on dictionary learning and wavelet decomposition.

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