论文标题
DEPAC：大脑网络中相位振幅耦合的可靠估计
REPAC: Reliable estimation of phase-amplitude coupling in brain networks
论文作者
论文摘要
最近的证据揭示了跨频耦合，尤其是相位振幅耦合（PAC）是大脑完成各种高级认知和感觉函数的重要策略。但是，解码PAC仍然具有挑战性。该贡献提出了REPAC，这是一种用于建模和检测EEG信号中PAC事件的可靠且可靠的算法。首先，我们解释了类似PAC的脑电图信号的合成，并特别注意表征PAC的最关键参数，即SNR，调制指数，耦合的持续时间。其次，详细介绍了DEPAC。我们使用计算机模拟生成一组随机的PAC状EEG信号，并针对基线方法测试REPAC的性能。即使使用SNR的现实值，例如-10 dB，epac也显示出胜过基线方法。它们都达到99％的精度水平，但是REPAC可显着提高敏感性，从20.11％到65.21％，具有可比的特异性（约99％）。 DEPAC还应用于实际的脑电图信号，显示了初步令人鼓舞的结果。
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.
