Independent Component Analysis-motivated Approach to Classificatory Decomposition of Cortical Evoked Potentials

Background: Independent Component Analysis (ICA) proves to be useful in the analysis of neural activity, as it allows for identification of distinct sources of activity. Applied to measurements registered in a controlled setting and under exposure to an external stimulus, it can facilitate analysis of the impact of the stimulus on those sources. The link between the stimulus and a given source can be verified by a classifier that is able to "predict" the condition a given signal was registered under, solely based on the components. However, the ICA's assumption about statistical independence of sources is often unrealistic and turns out to be insufficient to build an accurate classifier. Therefore, we propose to utilize a novel method, based on hybridization of ICA, multiobjective evolutionary algorithms (MOEA), and rough sets (RS), that attempts to improve the effectiveness of signal decomposition techniques by providing them with "classification-awareness." ...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Tomasz G. Smolinski, Roger Buchanan, Grzegorz M. Boratyn, Mariofanna G. Milanova, Astrid A. Prinz
• Keywords: Analysis, BMCBI 2006, Independent Component Analysis, Neural Activity