This research presents a new method for detecting obsessive–compulsive disorder (OCD) based on time–frequency analysis of multi-channel electroencephalogram (EEG) signals using the multi-variate synchrosqueezing transform (MSST). With the evolution of multi-channel sensor implementations, the employment of multi-channel techniques for the extraction of features arising from multi-channel dependency and mono-channel characteristics has become common. MSST has recently been proposed as a method for modeling the combined oscillatory mechanisms of multi-channel signals. It makes use of the concepts of instantaneous frequency (IF) and bandwidth. Electrophysiological data, like other nonstationary signals, necessitates both joint time–frequency analysis and independent time and frequency domain studies. The usefulness and effectiveness of a multi-variate, wavelet-based synchrosqueezing algorithm paired with a band extraction method are tested using electroencephalography data obtained from OCD patients and control groups in this research. The proposed methodology yields substantial results when analyzing differences between patient and control groups.

中文翻译：

---

基于多分辨率同步压缩变换的强迫症诊断策略

本研究提出了一种检测强迫症 (OCD) 的新方法，该方法基于使用多变量同步压缩变换 (MSST) 对多通道脑电图 (EEG) 信号进行时频分析。随着多通道传感器实现的发展，采用多通道技术来提取由多通道依赖性和单通道特性引起的特征已变得普遍。MSST 最近被提议作为一种对多通道信号的组合振荡机制进行建模的方法。它利用了瞬时频率 (IF) 和带宽的概念。与其他非平稳信号一样，电生理数据需要联合时频分析以及独立的时域和频域研究。在本研究中，使用从强迫症患者和对照组获得的脑电图数据测试了多变量、基于小波的同步压缩算法与频带提取方法的有效性和有效性。在分析患者和对照组之间的差异时，所提出的方法会产生实质性的结果。