Multi-objective evolutionary algorithms and rough sets for decomposition and analysis of cortical evoked potentials
Mariofanna G. Milanova, Tomasz G. Smolinski, Grzegorz M. Boratyn, R. Buchanan, Astrid A. Prinz · 2006
Signal decomposition techniques prove to be useful in the analysis of neural activity, as they allow for identification of supposedly distinct neuronal structures (i.e., sources of activity). Applied to measurements of brain activity in a controlled setting as well as under exposure to an external stimulus, they allow for analysis of the impact of the stimulus on those structures. The link between the stimulus and a given source can be confirmed by a classifier that is able to predict if a given signal was registered under one or the other condition, solely based on the components. Very often, however, statistical criteria used in traditional decomposition techniques turn out to be insufficient to build an accurate classifier. Therefore, we propose to utilize a novel hybrid technique based on multi-objective evolutionary algorithms (MOEA) and rough sets (RS) that will perform decomposition in the light of the classification problem itself.