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Author Notes:

Tomasz G Smolinski tomasz.smolinski@emory.edu Address: Department of Biology, Emory University, Atlanta, Georgia, USA

RB performed the underlying neurophysiological experiments.

TGS and GMB implemented the testing software and performed the simulations.

TGS, RB, GMB, MM, and AAP analyzed the results.

All authors read and approved the final manuscript.

Subjects:

Research Funding:

Research partially supported by: NIH Grant Number P20 RR-16460 from the IDeA Networks of Biomedical Research Excellence (INBRE) Program of the National Center for Research Resources and the Arkansas Bioscience Institute and NIH Grant Number 301-435-0888 from the National Center for Research Resources (NCRR).

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

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Journal Title:

BMC Bioinformatics

Volume:

Volume 7, Number Suppl 2

Publisher:

, Pages S8-S8

Type of Work:

Article | Final Publisher PDF

Abstract:

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, multi-objective evolutionary algorithms (MOEA), and rough sets (RS), that attempts to improve the effectiveness of signal decomposition techniques by providing them with classification-awareness. Results The preliminary results described here are very promising and further investigation of other MOEAs and/or RS-based classification accuracy measures should be pursued. Even a quick visual analysis of those results can provide an interesting insight into the problem of neural activity analysis. Conclusion We present a methodology of classificatory decomposition of signals. One of the main advantages of our approach is the fact that rather than solely relying on often unrealistic assumptions about statistical independence of sources, components are generated in the light of a underlying classification problem itself.

Copyright information:

© 2006 Smolinski et al; licensee BioMed Central Ltd.

This is an Open Access work distributed under the terms of the Creative Commons Attribution 2.0 Generic License (http://creativecommons.org/licenses/by/2.0/).

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