Non-linear auto-regressive models for cross-frequency coupling in neural time series

We address the issue of reliably detecting and quantifying cross-frequency coupling (CFC) in neural time series. Based on non-linear auto-regressive models, the proposed method provides a generative and parametric model of the time-varying spectral content of the signals. As this method models the e...

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Detalles Bibliográficos
Autores principales: Dupré la Tour, Tom, Tallot, Lucille, Grabot, Laetitia, Doyère, Valérie, van Wassenhove, Virginie, Grenier, Yves, Gramfort, Alexandre
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5739510/
https://www.ncbi.nlm.nih.gov/pubmed/29227989
http://dx.doi.org/10.1371/journal.pcbi.1005893

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5739510/
https://www.ncbi.nlm.nih.gov/pubmed/29227989
http://dx.doi.org/10.1371/journal.pcbi.1005893

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