REPAC: Reliable estimation of phase-amplitude coupling in brain networks
This work addresses a specific problem in neuroscience for researchers analyzing brain networks, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the challenge of decoding phase-amplitude coupling (PAC) in EEG signals by introducing REPAC, a reliable algorithm that outperforms a baseline method, improving sensitivity from 20.11% to 65.21% while maintaining specificity around 99%.
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.