Dictionary Learning has proven to be a powerful tool for many image processing tasks, where atoms are typically defined on small image patches. As a drawback, the dictionary only encodes basic structures. In addition, this approach treats patches of different locations in one single set, which means a loss of informa-tion when features are well-aligned across signals. This is the case, for instance, in multi-trial magneto-or electroencephalography (M/EEG). Learning the dictio-nary on the entire signals could make use of the alignment and reveal higher-level features. In this case, however, small misalignments or phase variations of fea-tures would not be compensated for. In this paper, we propose an extension to the common dictionary learning framework to overcome these limitations by allowing atoms to adapt their position across signals. The method is validated on simulated and real neuroelectric data.