Independent component analysis-motivated approach to classificatory decomposition of cortical evoked potentials

BMC Bioinformatics

Volumn 7, Issue SUPPL.2, 2006, Pages

  Smolinski, Tomasz G ^a   Buchanan, Roger ^b   Boratyn, Grzegorz M ^c   Milanova, Mariofanna ^d   Prinz, Astrid A ^a  

^a EMORY UNIVERSITY   (United States)

^b ARKANSAS STATE UNIVERSITY   (United States)

^c UNIVERSITY OF LOUISVILLE   (United States)

^d University of Arkansas   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ACCURACY; EXTERNAL STIMULUS; INDEPENDENT COMPONENT ANALYSIS(ICA); INDEPENDENT COMPONENTS; MULTI OBJECTIVE EVOLUTIONARY ALGORITHMS; SIGNAL DECOMPOSITION TECHNIQUE; STATISTICAL INDEPENDENCE; VISUAL ANALYSIS;

BRAIN; ELECTROPHYSIOLOGY; EVOLUTIONARY ALGORITHMS; NEURONS;

INDEPENDENT COMPONENT ANALYSIS;

ACCURACY; ANALYTICAL ERROR; ANIMAL EXPERIMENT; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; COMPUTER PROGRAM; CONTROLLED STUDY; EVOKED CORTICAL RESPONSE; EVOLUTIONARY ALGORITHM; INDEPENDENT COMPONENT ANALYSIS; MATHEMATICAL COMPUTING; METHODOLOGY; NONHUMAN; RAT; ROUGH SET; SIGNAL PROCESSING; ALGORITHM; ANIMAL; BIOLOGY; BRAIN; EVOKED RESPONSE; PHYSIOLOGY;

ALGORITHMS; ANIMALS; BRAIN; COMPUTATIONAL BIOLOGY; EVOKED POTENTIALS; RATS;

EID: 33947319931     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-7-S2-S8     Document Type: Article

References (24)

1. Mamiya N, Buchanan R, Wallace T, Skinner D, Garcia E: Nicotine suppresses the P 13 auditory evoked potential by acting on the pedunculopontine nucleusin the rat. Exp Brain Res 2005, 164:109-119.
2. Smolinski TG: Classificatory Decomposition for Time Series Classification and Clustering. In PhD thesis Univ. of Louisville, Louisville; 2004.
3. Smolinski TG, Boratyn GM, Milanova M, Zurada JM, Wrobel A: Evolutionary algorithms and rough sets-based hybrid approach to classificatory decomposition of cortical evoked potentials. Lecture Notes in Artificial Intelligence 2002, 2475:621-628.
4. Smolinski TG, Milanova M, Boratyn GM, Buchanan R, Prinz A: Multi-objective evolutionary algorithms and rough sets for decomposition and analysis of cortical evoked potentials. In Proc. IEEE International Conference on Granular Computing Atlanta, GA; 2006:635-638.
5. Smolinski TG, Boratyn GM, Milanova M, Buchanan R, Prinz A: Hybridization of independent component analysis, rough sets, and multi-objective evolutionary algorithms for classificatory decomposition of cortical evoked potentials. Lecture Notes in Bioinformatics 2006, 4146:174-183.
6. Schaffer JD: Some Experiments in Machine Learning Using Vector Evaluated Genetic Algorithms. In PhD thesis Vanderbilt University, 1984.
7. Deb K: Multi-Objective Optimization Using Evolutionary Algorithms Wiley; 2001.
8. Amari S, Cichocki A, Yang HH: A new learning algorithm for blind signal separation. In Advances in Neural Information Processing Systems Volume 8. Edited by: Touretzky DS, Mazer MC, Hasselmo ME. The MIT Press; 1996:757-763.
9. Hyvarinen A, Oja E: Independent component analysis: Algorithms and applications. Neural Networks 2000, 13(4):411-430.
10. Hyvarinen A: Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans on Neural Networks 1999, 10(3):626-634.
11. Hurri J: The FastICA package for MATLAB v. 2.5. 2005 [http://www.cis.hut.fi/projects/ica/fastica/].
12. Goldberg DE: Genetic Algorithms in Search Optimization and Machine Learning Addison-Wesley; 1989.
13. Srinivas N, Deb K: Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation 1994, 2(3):221-248.
14. Laumanns M, Zitzler E, Thiele L: A unified model for multi-objective evolutionary algorithms with elitism. In Proceedings of the 2000 Congress on Evolutionary Computation CECOO IEEE Press; 2000:46-53.
15. Pawlak Z: Rough Sets - Theoretical Aspects of Reasoning About Data Kluwer 1991.
16. Wróblewski J: Finding minimal reducts using genetic algorithms. In Proc 2nd Annual Joint Conference on Information Sciences Wrightsville Beach, NC, 1995:186-189.
17. Gawryś M, Sienkiewicz J: RSL - The Rough Set Library version 2.0. In Tech rep Warsaw University of Technology, Poland; 1994.
18. Milanova M, Smolinski TG, Boratyn GM, Zurada JM, Wrobel A: Correlation kernel analysis and evolutionary algorithm-based modeling of the sensory activity within the rat's barrel cortex. Lecture Notes in Computer Science 2002, 2388:198-212.
19. Kreyszig E: Introductory Functional Analysis with Applications Wiley, 1978.
20. Johnson D: Approximation algorithms for combinatorial problems. J of Computer and System Sciences 1974, 9:256-278.
21. Zurada J: Introduction to Artificial Neural Systems West Publishing Company, 1992.
22. Flury B: Common Principal Components and Related Multivariate Models Wiley, 1988.
23. Olshausen B, Field DJ: Sparse coding with an overcomplete basis set: A strategy employed by VI? Vision research 37(23):.
24. Lewicki M, Sejnowski T: Learning overcomplete representations. Neural Computation 2000, 12:337-365.