The time-varying cross-spectrum method has been used to effectively study transient and dynamic brain functional connectivity between non-stationary electroencephalography (EEG) signals. Wavelet-based cross-spectrum is one of the most widely implemented methods, but it is limited by the spectral leakage caused by the finite length of the basic function that impacts the time and frequency resolutions. This paper proposes a new time-frequency brain functional connectivity analysis framework to track the non-stationary association of two EEG signals based on a Revised Hilbert-Huang Transform (RHHT). The framework can estimate the cross-spectrum of decomposed components of EEG, followed by a surrogate significance test. The results of two simulation examples demonstrate that, within a certain statistical confidence level, the proposed framework outperforms the wavelet-based method in terms of accuracy and time-frequency resolution. A case study on classifying epileptic patients and healthy controls using interictal seizure-free EEG data is also presented. The result suggests that the proposed method has the potential to better differentiate these two groups benefiting from the enhanced measure of dynamic time-frequency association.

中文翻译：

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修正的希尔伯特-黄变换，用于跟踪脑电信号的非平稳关联

时变互谱方法已用于有效研究非平稳脑电图（EEG）信号之间的瞬时和动态大脑功能连接。基于小波的互谱是实现最广泛的方法之一，但是它受到基本函数有限长度（影响时间和频率分辨率）引起的频谱泄漏的限制。本文提出了一种新的时频大脑功能连通性分析框架，该框架基于修订的希尔伯特-黄（HHT）变换来跟踪两个脑电信号的非平稳关联。该框架可以估计脑电图分解成分的交叉谱，然后进行替代显着性检验。两个仿真示例的结果表明，在一定的统计置信度内，提出的框架在准确性和时频分辨率方面均优于基于小波的方法。还提出了使用无发作间期脑电图数据对癫痫患者和健康对照进行分类的案例研究。结果表明，所提出的方法有可能从动态时频关联的增强测量中受益，从而更好地区分这两组。