Abstract

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.