Abstract
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.
Highlights
E LECTROENCEPHALOGRAPHY (EEG) is a powerful technique that can noninvasively study the electrophysiological brain dynamics with high temporal accuracy
This paper proposes a novel data analysis framework to track the dynamic association between EEG channels in the time-frequency domain
This framework aims to characterize frequency fluctuations with a high time-frequency resolution and capture the dynamic association in the frequency domain using a new indicator, called Revised Hilbert-Huang Transformation cross-spectrum. It has been demonstrated by the two simulation examples that, compared to Continuous Wavelet Transform (CWT), RHHT can estimate the frequency modulations more accurately, as well as detect the abrupt change of frequency, amplitude, and corresponding associations
Summary
E LECTROENCEPHALOGRAPHY (EEG) is a powerful technique that can noninvasively study the electrophysiological brain dynamics with high temporal accuracy. Unlike WT with sinusoidal or physiologically defined wavelets, EMD (Empirical Mode Decomposition) is more intuitive and adaptive without any template assumption of the analysed signal [7], which makes it more suitable for describing non-stationary asymmetric and non-linear characteristics of EEG. Since the decomposition is based on the characteristics of the local time scale, with the Hilbert Transform, the IMFs generate instantaneous frequencies as functions of time that separately estimates dynamic structures of different transient information. Higher time-frequency resolution means better capturing the dynamic non-stationarity and non-linearity of EEG signals [2], which translates to a more reliable estimation of time-varying brain functional connectivity. 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 effectiveness of this proposed method is validated through simulated and clinical EEG data
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