LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Wei, H.L.; Liu, J.; Billings, S.A. (2008)
Publisher: Automatic Control and Systems Engineering, University of Sheffield
Languages: English
Types: Book
Subjects:
A new time-varying autoregressive (TVAR) modelling approach is proposed for nonstationary signal processing and analysis, with application to EEG data modelling and power spectral estimation. In the new parametric modelling framework, the time-dependent coefficients of the TVAR model are represented using a novel multi-wavelet decomposition scheme. The time-varying modelling problem is then reduced to regression selection and parameter estimation, which can be effectively resolved by using a forward orthogonal regression algorithm. Two examples, one for an artificial signal and another for an EEG signal, are given to show the effectiveness and applicability of the new TVAR modelling method.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Adeli, H., Zhou, Z. and Dadmehr, N. (2003) 'Analysis of EEG records in an epileptic patient using wavelet transform', Journal of Neuroscience Methods, Vol.43, No.1, pp.69-87.
    • Aguirre, L. A. and Billings, S. A. (1995) 'Retrieving dynamical invariants from chaotic data using NARMAX models', International Journal of Bifurcation and Chaos, Vol. 5, No.2, pp.449-474.
    • Akaike, H. (1974) 'A new look at the statistical model identification', IEEE Transactions on Automatic Control, Vol. 19, No. 6, pp. 716-723.
    • Amir, N. and Gath, I. (1989) 'Segmentation of EEG during sleep using time-varying autoregressive modelling', Biological Cybernetics, Vol.61, No. 6, pp.447-455.
    • Andrzejak, R. G., Lehnertz, K., Rieke, C., Mormann, F., David, P., and Elger, C. E. (2001) 'Indications of nonlinear deterministic and finite dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state', Physical Review E, Vol.64, 061907.
    • Barlow, J. S. (1985) 'Methods of analysis of nonstationary EEGs, with emphasis on segmentation techniques-A comparative review', Journal of clinical neurophysiology, Vol.2, No.3, pp.267-304.
    • Billings, S. A., Chen, S. and Korenberg, M. J. (1989) 'Identification of MIMO non-linear systems using a forward-regression orthogonal estimator', International Journal of Control, Vol.49, No.6, pp. 2157-2189.
    • Billings, S. A. and Coca, D. (1999) 'Discrete wavelet models for identification and qualitative analysis of chaotic systems', International Journal of Bifurcation and Chaos, Vol.9, pp. 1263-1284.
    • Billings, S. A. and Wei, H. L. (2005a) 'A new class of wavelet networks for nonlinear system identification', IEEE Transactions on Neural Networks, Vol. 16, pp. 862-874.
    • Billings, S. A. and Wei, H. L. (2005b) 'The wavelet-NARMAX representation: a hybrid model structure combining polynomial models and multiresolution wavelet decompositions', International Journal of Systems Science, Vol. 36, No.3, pp. 137-152.
    • Billings, S. A. and Wei, H. L. (2007) 'Sparse model identification using a forward orthogonal regression algorithm aided by mutual information', IEEE Transactions on Neural Networks, Vol.18, No.1, pp. 306-310.
    • Billings, S. A., Wei, H. L. and Balikhin, M. A. (2007) 'Generalized multiscale radial basis function networks', Neural Networks, Vol. 20, No. 10), pp.1081-1094.
    • Blanco, S., Figliola, A. and Quiroga, R. Q. (1998) 'Time-frequency analysis of electroencephalogram series. III. Wavelet packets and information cost function', Physical Review E, Vol. 57, No. 1, pp.932-940.
    • Bohlin, T. (1977) 'Analysis of EEG signals with changing spectral using a short-word Kalman estimator', Mathematical Biosciences, Vol.35, pp.221-259.
    • Charbonnier, R., Barlaud, M., Alengrin, G. and Menea J. (1987) 'Results on AR-modelling of induced epileptogenesis', International Journal of Neuroscience, Vol.97, No.3-4, pp.149-167.
    • Moller, E., Schack, B., Arnold, M. and Witte, H. (2001) 'Instantaneous multivariate EEG coherence analysis by means of adaptive high-dimensional autoregressive models', Journal of Neuroscience Methods, Vol.105, No.2, pp143-158.
    • Muthuswamy, J. and Thakor, N. V. (1998) 'Spectral analysis methods for neurological signals', Journal of Neuroscience Methods, Vol. 83, pp. 1-14.
    • Niedzwiecki, M. (1988) 'Functional series modelling approach to identification of nonstationary stochastic systems', IEEE Transactions Automatic Control, Vol.33, pp.955-961.
    • Pachori, R. B. and Sircar, P. (2008) 'EEG signal analysis using FB expansion and second-order linear TVAR process', Signal Processing, Vol.88, pp.415-420.
    • Panzica, F., Canafoglia, L., Franceschetti, S., Binelli, S., Ciano, C., Visan, E. and Avanzini, G. (2003) 'Movement-activated myoclonus in genetically defined progressive myoclonic epilepsies: EEGEMG relationship estimated using autoregressive models', Clinical Neurophysiology, Vol.14, no.6, pp.1041-1052.
    • Pardey, J., Roberts, S. and Tarassenko, L. (1996) 'A review of parametric modeling techniques for EEG analysis', Medical Engineering and Physics, Vol. 18, No. 1, pp.2-11.
    • Pascualmarqui, R. D., Valdessosa, P. A. and Alvarezamador, A. (1988) 'A parametric model for multichannel EEG spectra', International Journal of Neuroscience, Vol.40, No.1-2, pp. 89-99.
    • Prado, R. and Huerta, G. (2002) 'Time-varying autoregressions with model order uncertainty', Journal of Time Series Analysis', Vol. 23, No.5, pp.599-618.
    • Praetorius, H. M., Bodenstein, G. and Creutzfeldt, O. D. (1977) Adaptive segmentation of EEG records-new approach to automatic EEG analysis', Electroencephalography and Clinical Neurophysiology, Vol.42, No.1, pp.84-94.
    • Quiroga, R. Q., Blanco, S. and Rosso, O. A. (1997) 'Searching for hidden information with Gabor transform in generalized tonic-clonic seizures', Electroencephalography and Clinical Neurophysiology, Vol. 103, No. 4, pp.434-439.
    • Quiroga, R. Q., Garcia, H. and Rabinowicz, A. (2002) 'Frequency evolution during tonic-clonic seizures', Electromyography and Clinical Neurophysiology, Vol. 42, No. 6, pp.332-331.
    • Schiff, S. J., Aldroubi, A. Unser, M. and Sato, S. (1994) 'Fast wavelet transformation of EEG', Electroencephalography and Clinical Neurophysiology, Vol. 91, No. 6, pp.442-455.
    • Schwarz, G. (1978) 'Estimating the dimension of a model', The Annals of Statistics, Vol.6, No.2, pp.461-464.
    • Subasi, A. (2007) 'Selection of optimal AR spectral estimation method for EEG signal using CramerRao bound', Computers in Biology and Medicine, Vol. 37, No.2, pp. 183-194.
    • Subasi, A. Alkan, A., Koklukaya, E., and Kiymik, M. K. (2005) 'Wavelet neural network classification of EEG signals by using AR model with MLE preprocessing', Neural Networks, Vol. 18, No.7, pp. 985-997.
    • Tarvainen, M. P., Hiltunen, J. K., Ranta-aho, P. O. and Karjalainen, P. A. (2004) 'Estimation of nonstationary EEG with Kalman smoother approach: An application to event-related synchronization', IEEE Transactions on Biomedical Engineering, Vol.51, No.3, pp.516-524.
    • Tarvainen, M. P., Georgiadis, S. D., Ranta-aho, P. O. and Karjalainen, P. A.(2006) 'Time-varying analysis of heart rate variability signals with a Kalman smoother algorithm', Physical Measurement, Vol.27, No.3, pp.225-239.
    • Tseng, S-Y. , Chen, R-C., Chong, F-C. and Kuo, T-S. (1995) 'Evaluation of parametric methods in EEG signal analysis', Medical Engineering and Physics, Vol. 17, No.1, pp.71-78.
    • Wada, M., Ogawa, T., Sonoda, H. and Sato, K. (1996) 'Development of relative power contribution ratio of the EEG in normal children: A multivariate autoregressive modeling approach', Electroencephalography and Clinical Neurophysiology, Vol.98, No.1, pp.69-75.
    • Wei, H. L. and Billings, S. A. (2002) 'Identification of time-varying systems using multi-resolution wavelet models', International Journal of Systems Science, Vol. 33, No.15, pp. 1217-1228.
    • Wei, H. L. and Billings, S. A. (2004) 'A unified wavelet-based modelling framework for nonlinear system identification: the WANARX model structure', International Journal of Control, Vol. 77, No.4, pp. 351-366.
    • Wei, H. L., Billings, S. A. and Balikhin, M. (2004) 'Analysis of the geomagnetic activity of the D-st index and self-affine fractals using wavelet transforms', Nonlinear Processes in Geophysics, Vol. 11, pp.303-312.
    • Wei, H. L., Billings, S. A. and Liu, J. (2004) 'Term and variable selection for nonlinear system identification', International Journal of Control, Vol. 77, No. 1, pp. 86-110.
    • Wei, H. L. and Billings, S. A. (2006a) 'Long term prediction of nonlinear time series using multiresolution wavelet models', International Journal of Control, Vol.79, No.6, 569-580.
    • Wei, H. L. and Billings, S. A. (2006b) 'An efficient nonlinear cardinal B-spline model for high tide forecasts of at the Venice lagoon', Nonlinear Processes in Geophysics, Vol. 13, pp.577-584.
    • Wei, H. L. and Billings, S. A. (2007) 'A comparative study of global wavelet and polynomial models for non-linear regime-switching systems', International Journal of Modelling, Identification and Control, Vol. 2, No. 4, pp. 273-282.
    • Wei, H. L. and Billings, S. A. (2008) 'Model structure selection using an integrated forward orthogonal search algorithm assisted by squared correlation and mutual information', International Journal of Modelling, Identification and Control, (in press).
    • Zhou, S. M., Gan, J. Q. and Sepulveda, F. (2008) 'Classifying mental tasks based on features of higher-order statistics from EEG signals in brain-computer interface', Information Sciences, Vol. 178, No.6, pp. 1629-1640.
    • Zhu, Q. M. and Billings, S. A. (1996) 'Fast orthogonal identification of nonlinear stochastic models and radial basis function networks', International Journal of Control, Vol.64, No.5, pp. 871-886.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article