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

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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Bibliographic Details
Main Authors: Tom Dupré la Tour, Lucille Tallot, Laetitia Grabot, Valérie Doyère, Virginie van Wassenhove, Yves Grenier, Alexandre Gramfort
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2017-12-01
Series:PLoS Computational Biology
Online Access:http://europepmc.org/articles/PMC5739510?pdf=render

Internet

http://europepmc.org/articles/PMC5739510?pdf=render

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