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This paper presents a novel approach to decomposition and classification of rat's cortical evoked potentials (EPs). The decomposition is based on learning of a sparse set of basis functions using Evolutionary Algorithms (EAs). The basis functions are generated in a potentially overcomplete dictionary of the EP components according to a probabilistic model of the data. Compared to the traditional, statistical signal decomposition techniques, this allows for a number of basis functions greater than the dimensionality of the input signals, which can be of a great advantage. However, there arises an issue of selecting the most significant components from the possibly overcomplete collection. This is especially important in classification problems performed on the decomposed representation of the data, where only those components that provide a substantial discernibility between EPs of different groups are relevant. In this paper, we propose an approach based on the Rough Set theory's (RS) feature selection mechanisms to deal with this problem. We design an EA and RS-based hybrid system capable of signal decomposition and, based on a reduced component set, signal classification.