Event-related functional magnetic resonance imaging (ER-fMRI) refers to the blood oxygen level-dependent (BOLD) signal in response to a short stimulus followed by a long period of rest. These paradigms have become more popular in the last few years due to some advantages over standard block techniques. Most of the analysis of the time series generated in such exams is based on a model of specific hemodynamic response function. In this paper we propose a new method for the analysis of ER-fMRI based in a specific aspect of information theory: the entropy of a signal using the Shannon formulation, which makes no assumption about the shape of the response. The results show the ability to discriminate between activated and resting cerebral regions for motor and visual stimuli. Moreover, the results of simulated data show a more stable pattern of the method, if compared to typical algorithms, when the signal to noise ratio decreases.