A Revised Hilbert-Huang Transformation to Track Non-Stationary Association of Electroencephalography Signals

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