A better way to define and describe Morlet wavelets for time-frequency analysis.
Neuroimage
; 199: 81-86, 2019 10 01.
Article
em En
| MEDLINE
| ID: mdl-31145982
ABSTRACT
Complex Morlet wavelets are frequently used for time-frequency analysis of non-stationary time series data, such as neuroelectrical signals recorded from the brain. The crucial parameter of Morlet wavelets is the width of the Gaussian that tapers the sine wave. This width parameter controls the trade-off between temporal precision and spectral precision. It is typically defined as the "number of cycles," but this parameter is opaque, and often leads to uncertainty and suboptimal analysis choices, as well as being difficult to interpret and evaluate. The purpose of this paper is to present alternative formulations of Morlet wavelets in time and in frequency that allow parameterizing the wavelets directly in terms of the desired temporal and spectral smoothing (expressed as full-width at half-maximum). This formulation provides clarity on an important data analysis parameter, and can facilitate proper analyses, reporting, and interpretation of results. MATLAB code and sample data are provided.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Córtex Cerebral
/
Interpretação Estatística de Dados
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Eletroencefalografia
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Neuroimagem Funcional
/
Modelos Teóricos
Limite:
Humans
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article