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![arxiv：2011.06878v1 [eess.sp] 2020年11月13日](/simg/g:\will\search\icon\source\5\585342ee315b57f8f60eceef20306d0e67394a79.webp)
arxiv：2011.06878v1 [eess.sp] 2020年11月13日
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官功能的重要策略。但是，解码PAC仍然具有挑战性。此贡献提出了REPAC，这是一种可靠且可靠的算法，用于建模和检测EEG信号中的PAC事件。首先，我们解释了类似PAC的脑电图信号的合成，并特别注意了表征PAC的最批评参数，即SNR，调制指数，耦合持续时间。第二，详细介绍了REPAC。我们使用计算机模拟生成一组随机的PAC状EEG信号，并在基线方法方面测试REPAC的性能。depac被证明即使具有snr的现实值，例如-10 dB。它们都达到了99％的精度水平，但是REPAC从20位导致了剂量现场的显着改善。11％至65。21％，具有可比的特定山脉（约99％）。DEPAC也适用于真正的脑电图信号，显示了初步令人鼓舞的结果。索引项 - 相位振幅耦合，脑网作品，depac，调制，脑电图
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