So far, the most recognized software to analyze data from fMRI experiments is Statistical Parametric Mapping (SPM). But 6 years ago an alternative approach based on spatial wavelet transform instead of Gaussian pre-filtering (as it is the case with SPM) has been proposed. It is based on the sparsity provided by the representation in the wavelet domain that increases the SNR as the noise remains evenly distributed in this domain.

We describe a scientific work on a new manner of exploiting fMRI data based on the latest idea of introducing redundant wavelet transforms. Redundant wavelet processing and frame theories more generally suggested hopes of achieving even finer results than with classical non-redundant wavelet.