[Eeglablist] Mutual Information-based Phase-Amplitude Coupling (miPAC)

[Scott Makeig]

Ramon Martinez-Cancino et al. have published a new paper on a mutual
information-based approach to estimating phase-amplitude coupling (PAC)
between high-frequency power or amplitude and low-frequency phase in a
signal. The new method has advantages with respect to time resolution for
both continuous and epoched data. An EEGLAB plug-in PAC toolbox will be
released soon. Read the new article for free at

https://authors.elsevier.com/c/1XyiV3lc~r3J6u

Abstract

Here we demonstrate the suitability of a local mutual information measure
for estimating the temporal dynamics of cross-frequency coupling (CFC) in
brain electrophysiological signals. In CFC, concurrent activity streams in
different frequency ranges interact and transiently couple. A particular
form of CFC, phase-amplitude coupling (PAC), has raised interest given the
growing amount of evidence of its possible role in healthy and pathological
brain information processing. Although several methods have been proposed
for PAC estimation, only a few have addressed the estimation of the
temporal evolution of PAC, and these typically require a large number of
experimental trials to return a reliable estimate. Here we explore the use
of mutual information to estimate a PAC measure (MIPAC) in both continuous
and event-related multi-trial data. To validate these two applications of
the proposed method, we first apply it to a set of simulated
phase-amplitude modulated signals and show that MIPAC can successfully
recover the temporal dynamics of the simulated coupling in either
continuous or multi-trial data. Finally, to explore the use of MIPAC to
analyze data from human event-related paradigms, we apply it to an actual
event-related human electrocorticographic (ECoG) data set that exhibits
strong PAC, demonstrating that the MIPAC estimator can be used to
successfully characterize amplitude-modulation dynamics in
electrophysiological data.
-- 
Scott Makeig, Research Scientist and Director, Swartz Center for
Computational Neuroscience, Institute for Neural Computation, University of
California San Diego, La Jolla CA 92093-0961, http://sccn.ucsd.edu/~scott
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