REPAC: Reliable estimation of phase-amplitude coupling in brain networks

TL;DR

REPAC is a new algorithm that reliably detects phase-amplitude coupling in EEG signals, outperforming existing methods especially in low signal-to-noise ratio conditions, and shows promising results on real data.

Contribution

The paper introduces REPAC, a robust and accurate method for detecting phase-amplitude coupling in EEG signals, improving sensitivity over baseline methods.

Findings

REPAC achieves around 99% accuracy in simulations.

REPAC significantly improves sensitivity from 20.11% to 65.21%.

Preliminary real EEG analysis shows promising results.

Abstract

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.