Obsessive–compulsive disorder (OCD) is one of the neuropsychiatric disorders qualified by intrusive and iterative annoying thoughts and mental attitudes that are activated by these thoughts. In recent studies, advanced signal processing techniques have been favored to diagnose OCD. This research suggests four different measurements; intrinsic phase-locked value, intrinsic coherence, intrinsic synchronization likelihood, and intrinsic visibility graph similarity that quantifies the synchronization level and complexity in electroencephalography (EEG) signals. This intrinsic synchronization is achieved by utilizing Multivariate Empirical Mode Decomposition (MEMD), a data-driven method that resolves nonlinear and nonstationary data into their intrinsic mode functions. Our intrinsic technique in this study demonstrates that MEMD-based synchronization analysis gives us much more detailed knowledge rather than utilizing the synchronization method alone. Furthermore, the nonlinear synchronization method presents more consistent results considering OCD heterogeneity. Statistical evaluation using sample [Formula: see text]-test and [Formula: see text]-test has shown the significance of such new methodology.

中文翻译：

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强迫症大脑活动的内在同步分析

强迫症 (OCD) 是一种神经精神疾病，由这些想法激活的侵入性和反复烦人的想法和心理态度所限定。在最近的研究中，先进的信号处理技术有利于诊断强迫症。这项研究提出了四种不同的测量方法；内在锁相值、内在相干性、内在同步似然性和内在可见性图相似性，量化脑电图 (EEG) 信号中的同步水平和复杂性。这种内在同步是通过利用多元经验模式分解 (MEMD) 来实现的，MEMD 是一种数据驱动的方法，可将非线性和非平稳数据解析为其内在模式函数。我们在本研究中的内在技术表明，基于 MEMD 的同步分析为我们提供了更详细的知识，而不是单独使用同步方法。此外，考虑到 OCD 的异质性，非线性同步方法呈现出更一致的结果。使用样本[公式：见文本]-测试和[公式：见文本]-测试的统计评估表明了这种新方法的重要性。