High-resolution time–frequency representation of EEG data using multi-scale wavelets
| Autor: | Mei-Lin Luo, Ke Li, Weigang Cui, Lina Wang, Yang Li |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
business.industry Estimation theory System identification Pattern recognition 02 engineering and technology Mutual information Computer Science Applications Theoretical Computer Science Wavelet packet decomposition Time–frequency analysis 020901 industrial engineering & automation Wavelet Autoregressive model Time–frequency representation Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Mathematics |
| Zdroj: | International Journal of Systems Science. 48:2658-2668 |
| ISSN: | 1464-5319 0020-7721 |
| DOI: | 10.1080/00207721.2017.1340986 |
| Popis: | An efficient time-varying autoregressive (TVAR) modelling scheme that expands the time-varying parameters onto the multi-scale wavelet basis functions is presented for modelling nonstationary signals and with applications to time–frequency analysis (TFA) of electroencephalogram (EEG) signals. In the new parametric modelling framework, the time-dependent parameters of the TVAR model are locally represented by using a novel multi-scale wavelet decomposition scheme, which can allow the capability to capture the smooth trends as well as track the abrupt changes of time-varying parameters simultaneously. A forward orthogonal least square (FOLS) algorithm aided by mutual information criteria are then applied for sparse model term selection and parameter estimation. Two simulation examples illustrate that the performance of the proposed multi-scale wavelet basis functions outperforms the only single-scale wavelet basis functions or Kalman filter algorithm for many nonstationary processes. Furthermore, an... |
| Databáze: | OpenAIRE |
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