Time-varying parametric modelling and time-dependent spectral characterisation with applications to EEG signals using multi-wavelets

Book English OPEN
Wei, H.L. ; Liu, J. ; Billings, S.A. (2008)
  • Publisher: Automatic Control and Systems Engineering, University of Sheffield

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.
  • References (44)
    44 references, page 1 of 5

    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.

  • Metrics
    0
    views in OpenAIRE
    0
    views in local repository
    379
    downloads in local repository

    The information is available from the following content providers:

    From Number Of Views Number Of Downloads
    White Rose Research Online - IRUS-UK 0 379
Share - Bookmark