A new NARX-based Granger linear and nonlinear casual influence detection method with applications to EEG data

Journal of Neuroscience Methods

Volumn 212, Issue 1, 2013, Pages 79-86

  Zhao, Yifan ^a   Billings, Steve A ^a   Wei, Hualiang ^a   He, Fei ^a   Sarrigiannis, Ptolemaios G ^b  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

^b ROYAL HALLAMSHIRE HOSPITAL   (United Kingdom)

Author keywords

Causality; Linearity; NARMAX; Nonlinearity

Indexed keywords

ALGORITHM; ARTICLE; DATA ANALYSIS; ELECTROENCEPHALOGRAM; EPILEPSY; HUMAN; LINEARITY; MATHEMATICAL PARAMETERS; MATHEMATICAL PHENOMENA; NONLINEAR AUTOREGRESSIVE WITH EXOGENOUS INPUT; NONLINEAR SYSTEM; PRIORITY JOURNAL;

BRAIN; BRAIN MAPPING; BRAIN WAVES; COMPUTER SIMULATION; ELECTROENCEPHALOGRAPHY; HUMANS; LINEAR MODELS; NONLINEAR DYNAMICS; SEIZURES; SIGNAL DETECTION, PSYCHOLOGICAL; TIME FACTORS;

EID: 84867748196     PISSN: 01650270     EISSN: 1872678X     Source Type: Journal    
DOI: 10.1016/j.jneumeth.2012.09.019     Document Type: Article

References (35)

1. Andrzajak R.G., Ledberg A., Deco G. Detecting event-related time-dependent directional couplings. New J Phys 2006, 8:6.
2. Annegers J.F. The epidemiology in epilepsy 1996, Williams & Wilkins, Baltimore.
3. Baek E, Brock W. A general test for nonlinear granger causality. Working Paper, University of Iowa; 1992.
4. Billings S.A., Chen S., Korenberg M. Identification of MIMO non-linear systems using a forward-regression orthogonal edtimator. Int J Control 1989, 49(6):2157-2189.
5. Billings S.A., Korenberg M., Chen S. Identification of non-linear output-affine systems using an orthogonal least squares algorithm. Int J Syst Sci 1988, 19:1559-1568.
6. Billings S.A., Wei H.L. Sparse model identification using a forward orthogonal regression algorithm aided by mutual information. IEEE Trans Neural Network 2007, 18(1):306-310.
7. Billings S.A., Wei H.L. An adaptive orthogonal search algorithm for model subset selection and nonlinear system identification. Int J Control 2008, 81(5):714-724.
8. Blanco S., Quiroga R.Q., Rosso O.A., Kochen S. Time-frequency analysis of electroencephalogram series. Phys Rev E 1995, 51(3):2624-2631.
9. Blinowska K.J., Malinowski M. Non-linear and linear forecasting of the EEG time series. Biol Cybern 1991, 66:159-165.
10. Chen S., Billings S.A., Luo W. Orthogonal least squares methods and their application to nonlinear system identification. Int J Control 1989, 50(5):1873-1896.
11. Elger C.E., Lehnertz K. Seizure prediction by non-linear time series analysis of brain electrical activity. Eur J Neurosci 1998, 10:786-789.
12. Geweke J. Measures of conditional linear dependence and feedback between time series. J Am Stat Assoc 1984, 79:907-915.
13. Granger C.J. Investigating causal relations by econometric models and cross-spectral methods. Econometrica 1969, 37:424-438.
14. Hemmelmann D., Ungureanu M., Hesse W., Wustenberg T., Reichenbach J.R., Witte O.W., et al. Modelling and analysis of time-variant directed interrelations between brain regions based on bold-signals. Neuroimage 2009, 45:722-737.
15. Hoekstra B.P., Diks C.G., Allessie M., Degoede J. Nonlinear time series analysis: methods and applications to atrial fibrillation. Ann Ist Super Sanita 1992, 37(3):325-333.
16. Iasemidis L.D., Sackellares J.C. Chaos theory and epilepsy. Neuroscientist 1996, 2:118-126.
17. Leontaritis I.J., Billings S.A. Input-output parametric models for non-linear systems. Part I: Deterministic non-linear systems. Int J Control 1985, 41:303-328.
18. Leontaritis I.J., Billings S.A. Input-output parametric models for non-linear systems. Part II: Stochastic non-linear systems. Int J Control 1985, 41:329-344.
19. Lerner D.E. Monitoring changing dynamics with correlation integrals: case study of an epileptic seizure. Phys Rev A 1996, 97:563-576.
20. Mao K.Z., Billings S.A. Algorithms for minimal model structure detection in nonlinear dynamic system identification. Int J Control 1997, 68(2):311-330.
21. Martini M., Kranz T.A., Wangner T., Lehnertz K. Inferring directional interactions from transient signals with symbolic transfer entropy. Phys Rev E 2011, 83:011919.
22. McSharry P.E., Smith L.A., Tarassenko L. Comparison of predictability of epileptic seizures by a linear and a nonlinear method. IEEE Trans Biomed Eng 2003, 50(5):628-633.
23. McSharry P.E., Smith L.A., Tarassenko L. Prediction of epileptic seizures: are nonlinear methods relevant?. Nat Med 2003, 9:241-242.
24. Mormann F., Andrzejak R.G., Elge C.E., Lehnert K. Seizure prediction: the long and winding road. Brain 2007, 130:314-333.
25. Quyen L.V., Adam C., Martinerie J., Baulac M., Clemenceau S., Varela F. Spatio-temporal characterization of non-linear changes in intracranial activities prior to human temporal lobe seizures. Eur J Neurosci 2000, 12:2124-2134.
26. Saito Y., Harashima H. Tracking of information within multichannel EEG record? Causal analysis in EEG. Recent advances in EEG and EMG data processing 1981, 133-146. Elsevier, New York. N. Yamaguchi, K. Fujisawa (Eds.).
27. Sommerlade L., Thiel M., Platt B., Plano A., Riedel G., Grebogi C., et al. Inference of granger causal time-dependent influences in noisy multivariate time series. J Neurosci Methods 2012, 203:173-185.
28. Theiler J. Spurious dimension from correlation algorithms applied to limited time-series data. Phys Rev A 1986, 34(3):2427-2432.
29. Thomasson N., Hoeppner T.J., Webber C.L., Zbilut J.P. Recurrence quantification in epileptic EEGs. Phys Lett A 2001, 279:94-101.
30. Wangner T., Fell J., Lehnertz K. Detecting event-related time-dependent directional couplings. New J Phys 2010, 12:053031.
31. Wei H.L., Billings S.A., Zhao Y., Guo L.Z. Lattice dynamical wavelet neural networks implemented using particle swarm optimization for spatio-temporal system identification. IEEE Trans Neural Network 2009, 20(1):181-185.
32. Winterhalder M., Schelter B., Maiwald T., Aschenbrenner-Scheibe R., Brandt A., Schulze-Bonhage A., et al. Nonlinear dynamics in EEG from epileptic patients: is it possible to predict seizures?. AIP Conf Proc 2004, 742:216-221.
33. Zhao Y., Billings S.A. Neighbourhood detection using mutual information for the identification of cellular automata. IEEE Trans Syst Man Cybern B: Cybern 2006, 36(2):473-479.
34. Zhao Y., Wei H.L., Billings S.A. A new adaptive fast cellular automaton neighborhood detection and rule identification algorithm. IEEE Trans Syst Man Cybern B Cybern 2012, 42(4):1283-1287.
35. Zhu Q.M., Billings S.A. Fast orthogonal identification of nonlinear stochastic models and radial basis function neural networks. Int J Control 1996, 64(5):871-886.