2017
DOI: 10.1101/159731
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Non-linear Auto-Regressive Models for Cross-Frequency Coupling in Neural Time Series

Abstract: 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 entire spectrum simultaneously, it avoids the pitfalls related to incorrect filtering or the use of the Hilbert transform on wide-band signals. As the model is probabilistic, it also provides a score of the model "go… Show more

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Cited by 12 publications
(23 citation statements)
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References 98 publications
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“…For each frequency pair, the raw signal in each segment was exported from MATLAB into Python and filtered into a low frequency and high frequency signal using code adapted from (Dupré la Tour et al, 2017). Because the frequency pairs at which PAC occurs in our age group have not been previously well defined, we began by examining PAC across a range of frequencies.…”
Section: Computation Of Pac Metricsmentioning
confidence: 99%
“…For each frequency pair, the raw signal in each segment was exported from MATLAB into Python and filtered into a low frequency and high frequency signal using code adapted from (Dupré la Tour et al, 2017). Because the frequency pairs at which PAC occurs in our age group have not been previously well defined, we began by examining PAC across a range of frequencies.…”
Section: Computation Of Pac Metricsmentioning
confidence: 99%
“…Our dynamic estimation of PAC hence makes it possible to base inference on much shorter windows -as short as 6 seconds for slow 0.1-1Hz signals. Other novel models have been proposed to represent PAC, including driven autoregressive models (DAR) [24] and generalized linear models (GLM) [29]. As we saw earlier, SSP performs better than the DAR and standard approaches, particularly when the signal to noise is low.…”
Section: Discussionmentioning
confidence: 91%
“…In a recent paper, Dupré la Tour et al [24] designed an elegant nonlinear PAC formulation, described as a driven autoregressive (DAR) process, where the modulated signal is a polynomial function of the slow oscillation. The latter, referred to as the driver, is filtered out from the observation around a preset frequency and used to estimate DAR coefficients.…”
Section: Simulation Studiesmentioning
confidence: 99%
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