Method for unsupervised classification of multiunit neural signal recording under low signal-to-noise ratio

IEEE Transactions on Biomedical Engineering

Volumn 50, Issue 4, 2003, Pages 421-431

  Kim, Kyung Hwan ^a   Kim, Sung June ^b  

^a Samsung Advanced Institute of Technology (SAIT)   (South Korea)

^b Seoul National University   (South Korea)

Author keywords

Mixture of Gaussians; Neural spike sorting; Projection pursuit; Unsupervised classification

Indexed keywords

ALGORITHMS; PRINCIPAL COMPONENT ANALYSIS; PROBABILITY DENSITY FUNCTION; SIGNAL TO NOISE RATIO;

NEURAL SIGNAL RECORDING;

NEUROLOGY;

ABDOMINAL GANGLION; ALGORITHM; ANALYTIC METHOD; APLYSIA; ARTICLE; AUTOMATION; CLASSIFICATION; MATHEMATICAL ANALYSIS; NERVE CELL; NONHUMAN; NORMAL DISTRIBUTION; PRINCIPAL COMPONENT ANALYSIS; RAT; SIGNAL DETECTION; SIGNAL NOISE RATIO; SOMATOSENSORY CORTEX; SPIKE WAVE; STATISTICAL MODEL; ACTION POTENTIAL; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; COMPARATIVE STUDY; ELECTROPHYSIOLOGY; EVALUATION; METHODOLOGY; NERVE CELL NETWORK; PHYSIOLOGY; SIGNAL PROCESSING; STATISTICS; VALIDATION STUDY;

ACTION POTENTIALS; ALGORITHMS; ELECTROPHYSIOLOGY; MODELS, NEUROLOGICAL; MODELS, STATISTICAL; NERVE NET; NEURONS; PATTERN RECOGNITION, AUTOMATED; SIGNAL PROCESSING, COMPUTER-ASSISTED; STOCHASTIC PROCESSES;

EID: 0037388960     PISSN: 00189294     EISSN: None     Source Type: Journal    
DOI: 10.1109/TBME.2003.809503     Document Type: Article

References (26)

1. F. Rieke, D. Warland, R. De Ruyter Van Steveninck, and W. Bialek, Spikes: Exploring the Neural Code. Cambridge, MA: The MIT Press, 1996.
2. M. S. Lewicki, "A review of methods for spike sorting: The detection and classification of neural action potentials," Network: Computation Neural Syst., vol. 9, pp. R53-R78, 1998.
3. E. M. Schmidt, "Computer separation of multi-unit neuroelectric data: A review," J. Neurosci. Meth., vol. 12, pp. 95-111, 1984.
4. R. Chandra and L. M. Optican, "Detection, classification, and superposition resolution of action potentials in multiunit single channel recordings by an on-line real-time neural network," IEEE Trans. Biomed. Eng., vol. 44, pp. 403-412, May 1997.
5. K. H. Kim and S. J. Kim, "Neural spike sorting under nearly 0 dB signal-to-noise ratio using nonlinear energy operator and artificial neural network classifier," IEEE Trans. Biomed. Eng., vol. 47, pp. 1406-1411, Oct. 2000.
6. M. S. Fee, P. P. Mitra, and D. Kleinfeld, "Automatic sorting of multiple unit neuronal signals in the presence of anisotropic and non-Gaussian variability," J. Neurosci. Meth., vol. 69, pp. 175-188, 1996.
7. M. M. Sahani, "Variable models for neural data analysis," Ph.D. dissertation, California Inst. Technol., Pasadena, CA, 1999.
8. K. H. Kim and S. J. Kim, "Method for action potential detection from extracellular neural signal recording with low signal-to-noise ratio," IEEE Trans. Biomed. Eng., 2003, to be published.
9. R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis. New York: Wiley, 1973.
10. B. C. Wheeler and W. J. Heetderks, "A comparison of techniques for classification of multiple neural signals," IEEE Trans. Biomed. Eng., vol. BME-29, pp. 752-759, 1982.
11. P. J. Huber, "Projection pursuit," Ann. Statist., vol. 13, pp. 435-475, 1985.
12. M. Girolami, A. Cichocki, and S. I. Amari, "A common neural-network model for unsupervised exploratory data analysis and independent component analysis," IEEE Trans. Neural Network, vol. 9, pp. 1495-1501, 1998.
13. A. K. Jain, R. P. W. Duin, and J. Mao, "Statistical pattern recognition: A review," IEEE Trans. Pattern Anal. Machine Intell., vol. 22, pp. 4-37, Jan. 2000.
14. T. H. Yoon, E. J. Hwang, D. Y. Shin, S. I. Park, S. J. Oh, S. C. Jung, H. C. Shin, and S. J. Kim, "A micromachined silicon depth probe for multi-channel neural recording," IEEE Trans. Biomed. Eng., vol. 47, pp. 1082-1087, Aug. 2000.
15. M. H. Hayes, Statistical Digital Signal Processing and Modeling. New York: Wiley, 1996.
16. A. Hyvarinen, "Fast and robust fixed-point algorithms for independent component analysis," IEEE Trans. Neural Network, vol. 10, pp. 626-634, May 1999.
17. T. W. Lee, M. Girolami, and T. J. Sejnowski, "Independent component analysis using an extended infomax algorithm for mixed sub-Gaussian and super-Gaussian sources," Neural Computat., vol. 11, pp. 417-441, 1999.
18. D. G. Ruenberger, Optimization by Vector Space Methods. New York: Wiley, 1969.
19. M. S. Lewicki, "Bayesian modeling and classification of neural signals," Neural Computat., vol. 6, pp. 1005-1030, 1994.
20. X. L. Xie and G. A. Beni, "A validity measure for fuzzy clustering," IEEE Trans. Pattern Anal. Machine Intell., vol. 13, pp. 841-847, Aug. 1991.
21. D. A. Langan, J. W. Modestino, and J. Zhang, "Cluster validation for unsupervised stochastic model-based image segmentation," IEEE Trans. Imag. Processing, vol. 7, pp. 180-195, Feb. 1998.
22. M. A. Carreira-Perpinan, "Mode-finding for mixtures of Gaussian distribution," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, pp. 1318-1323, Nov. 2000.
23. C. M. Bishop, M. Svensen, and C. K. I. Williams, "GTM: The generative topographic mapping," Neural Computat., vol. 10, pp. 215-234, 1998.
24. T. Kohonen, Self-Organizing Maps, 2nd ed. Berlin, Germany: Springer-Verlag, 1997.
25. G. Zouridakis and D. C. Tam, "Identification of reliable spike templates in multi-unit extracellular recordings using fuzzy clustering," Comp. Meth. Prog. Biomed., vol. 61, pp. 91-98, 2000.
26. A. Hyvarinen, J. Karhunen, and E. Oja, Independent Component Analysis. New York: Wiley, 2001.