  Recent evidence has revealed cross-frequency coupling and, particularly,
phase-amplitude coupling (PAC) as an important strategy for the brain to
accomplish a variety of high-level cognitive and sensory functions. However,
decoding PAC is still challenging. This contribution presents REPAC, a reliable
and robust algorithm for modeling and detecting PAC events in EEG signals.
First, we explain the synthesis of PAC-like EEG signals, with special attention
to the most critical parameters that characterize PAC, i.e., SNR, modulation
index, duration of coupling. Second, REPAC is introduced in detail. We use
computer simulations to generate a set of random PAC-like EEG signals and test
the performance of REPAC with regard to a baseline method. REPAC is shown to
outperform the baseline method even with realistic values of SNR, e.g., -10 dB.
They both reach accuracy levels around 99%, but REPAC leads to a significant
improvement of sensitivity, from 20.11% to 65.21%, with comparable specificity
(around 99%). REPAC is also applied to a real EEG signal showing preliminary
encouraging results.
