2010
DOI: 10.1504/ijmic.2010.032802
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Time-varying parametric modelling and time-dependent spectral characterisation with applications to EEG signals using multiwavelets

Abstract: Abstract: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 timevarying modelling problem is then reduced to regression selection and parameter estimation, which can be effectively resolved by using a forward or… Show more

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Cited by 37 publications
(24 citation statements)
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References 64 publications
(60 reference statements)
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“…In this work, TV coefficients are expanded by multi-resolution cardinal B-splines wavelet series, and then the forward orthogonal least squares (OLS) algorithm [28]- [31], which have been proven to be a very effective to deal with multiple dynamical regressions problems, is applied to determine the forms in model (3). Detailed discussions -6 -of the procedure of the forward OLS can be found in [23], [28]- [31].…”
Section: Model Identification and Parameter Estimationmentioning
confidence: 99%
See 3 more Smart Citations
“…In this work, TV coefficients are expanded by multi-resolution cardinal B-splines wavelet series, and then the forward orthogonal least squares (OLS) algorithm [28]- [31], which have been proven to be a very effective to deal with multiple dynamical regressions problems, is applied to determine the forms in model (3). Detailed discussions -6 -of the procedure of the forward OLS can be found in [23], [28]- [31].…”
Section: Model Identification and Parameter Estimationmentioning
confidence: 99%
“…Compared with the RLS approach and the RLS approach with B-spline estimates, (8) where   at represents the estimates of coefficients   at in the TVARX model (3), and N is the length of the data set. The central objective of this paper for the EEG signals is to propose an empirical and data-based modelling framework from model identification that can produce an accurate but simple description of the dynamical relationships between different recording regions during brain activity.…”
Section: Simulation Examplementioning
confidence: 99%
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“…for time-varying parametric modelling [16], time-varying systems identification [17], time-varying parameter estimation [18] and time domain signal analysis [19]. In the literature the common method to analyze the time-varying system using discrete-time wavelet transform is to model the time-varying system with a time-invariant system firstly, because a general analysis of time-varying discrete-time wavelet transform (TV-DWT) is still missing.…”
Section: Introductionmentioning
confidence: 99%