A method for determinism in short time series, and its application to stationary EEG

IEEE Transactions on Biomedical Engineering

Volumn 49, Issue 11, 2002, Pages 1374-1379

  Jeong, Jaeseung ^a   Gore, John C ^b   Peterson, Bradley S ^c  

^a Korea University   (South Korea)

^b YALE UNIVERSITY   (United States)

^c Columbia University ^*  (United States)

Author keywords

Determinism; Smoothness; Stationary EEG; Time series

Indexed keywords

ELECTROENCEPHALOGRAPHY; PHYSIOLOGY; TIME SERIES ANALYSIS;

PHYSIOLOGICAL RECORDINGS;

BIOMEDICAL ENGINEERING;

ALGORITHM; ARTICLE; CALCULATION; ELECTROENCEPHALOGRAM; MATHEMATICAL ANALYSIS;

ADULT; ALGORITHMS; ELECTROENCEPHALOGRAPHY; FEMALE; HUMANS; MALE; MODELS, NEUROLOGICAL; MODELS, STATISTICAL; NONLINEAR DYNAMICS; SIGNAL PROCESSING, COMPUTER-ASSISTED; STATISTICS; STOCHASTIC PROCESSES; TIME FACTORS;

EID: 0036846032     PISSN: 00189294     EISSN: None     Source Type: Journal    
DOI: 10.1109/TBME.2002.804581     Document Type: Article

References (14)

1. G. Sugihara and R. M. May, "Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series," Nature, vol. 344, pp. 734-741, 1990.
2. D. T. Kaplan and L. Glass, "A direct test for determinism in a time series," Phys. Rev. Lett., vol. 68, pp. 427-430, 1992.
3. A. A. Tsonis and J. B. Elsner, "Nonlinear prediction as a way of distinguishing chaos from random fractal sequences," Nature, vol. 358, pp. 217-220, 1992.
4. R. Wayland, D. Broomley, D. Pickett, and A. Passamante, "Recognizing determinism in a time series," Phys. Rev. Lett., vol. 70, no. 5, pp. 580-582, 1993.
5. L. W. Salvino and R. Cawley, "Smoothness implies determinism: A method to detect it in time series," Phys. Rev. Lett., vol. 73, no. 8, pp. 1091-1094, 1994.
6. G. J. Ortega and E. Louis, "Smoothness implies determinism in time series: A measure based approach," Phys. Rev. Lett., vol. 81, pp. 4345-4348, 1998.
7. F. Takens, "Detecting strange attractors in turbulence in dynamical systems and turbulence," in Lecture Notes in Mathematics. Berlin, Germany: Springer-Verlag, 1981, vol. 898, pp. 366-381.
8. T. Schreiber and A. Schmitz, "Surrogate time series," Physica D, vol. 142, pp. 346-382, 2000.
9. M. E. Cohen, D. L. Hudson, and P. C. Deedwania, "Applying continuous chaotic modeling to cardiac signal analysis," IEEE Eng. Med. Biol. Mag., pp. 97-102, Sept./Oct. 1996.
10. J. Jeong, M. S. Kim, and S. Y. Kim, "Test for low-dimensional determinism in electroencephalograms," Phys. Rev. E., vol. 60, no. 1, pp. 831-837, 1999.
11. M. Gardner, Fractal Music Hypercards and More. San Francisco, CA: Freeman, 1992, pp. 1-23.
12. J. Jeong, M. K. Joung, and S. Y. Kim, "Quantification of emotion by nonlinear analysis of the chaotic dynamics of electroencephalograms during perception of 1/f music," Biol. Cybern., vol. 78, pp. 217-225, 1998.
13. K. J. Blinowska and M. Malinowski, "Nonlinear and linear forecasting of the EEG time series," Biol. Cybern., vol. 66, p. 159, 1991.
14. L. Glass, D. T. Kaplan, and J. E. Lewis, "Tests for deterministic dynamics in real and model neural networks," in Proc. 2nd Annu. Conf. Nonlinear Dynamical Analysis of the EEG, 1993, pp. 233-240.