ArticleFrontiers in neuroscience2021
Energy-Period Profiles of Brain Networks in Group fMRI Resting-State Data: A Comparison of Empirical Mode Decomposition With the Short-Time Fourier Transform and the Discrete Wavelet Transform.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Atypical brain function hierarchy in autism spectrum disorder: insights from a novel analytical approach based on neuronal oscillation pattern.European child & adolescent psychiatry · 2025Article
- An automated extraction of spectral-temporal and spatial-temporal features of EEG for emotion detection.Brain informatics · 2025Article
- Physiological Noise Filtering in Functional Near-Infrared Spectroscopy Signals Using Wavelet Transform and Long-Short Term Memory Networks.Bioengineering (Basel, Switzerland) · 2023Article
- Localizing Spectral Interactions in the Resting State Network Using the Hilbert-Huang Transform.Brain sciences · 2022Article
- Uterine Ultrasound Doppler Hemodynamics of Magnesium Sulfate Combined with Labetalol in the Treatment of Pregnancy-Induced Hypertension Using Empirical Wavelet Transform Algorithm.Computational intelligence and neuroscience · 2022Article
- Intelligent Extraction of Salient Feature From Electroencephalogram Using Redundant Discrete Wavelet Transform.Frontiers in neuroscience · 2022Article
- Seizure Prediction in EEG Signals Using STFT and Domain Adaptation.Frontiers in neuroscience · 2021Article
- Comparison of Empirical Mode Decomposition, Wavelets, and Different Machine Learning Approaches for Patient-Specific Seizure Detection Using Signal-Derived Empirical Dictionary Approach.Frontiers in digital health · 2021Article
- Topological reorganization of functional hubs in patients with Parkinson's disease with freezing of gait.Journal of neuroimaging : official journal of the American Society of NeuroimagingArticle
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Abstract
Traditionally, functional networks in resting-state data were investigated with linear Fourier and wavelet-related methods to characterize their frequency content by relying on pre-specified frequency bands. In this study, Empirical Mode Decomposition (EMD), an adaptive time-frequency method, is used to investigate the naturally occurring frequency bands of resting-state data obtained by Group Independent Component Analysis. Specifically, energy-period profiles of Intrinsic Mode Functions (IMFs) obtained by EMD are created and compared for different resting-state networks. These profiles have a characteristic distribution for many resting-state networks and are related to the frequency content of each network. A comparison with the linear Short-Time Fourier Transform (STFT) and the Maximal Overlap Discrete Wavelet Transform (MODWT) shows that EMD provides a more frequency-adaptive representation of different types of resting-state networks. Clustering of resting-state networks based on the energy-period profiles leads to clusters of resting-state networks that have a monotone relationship with frequency and energy. This relationship is strongest with EMD, intermediate with MODWT, and weakest with STFT. The identification of these relationships suggests that EMD has significant advantages in characterizing brain networks compared to STFT and MODWT. In a clinical application to early Parkinson's disease (PD) vs. normal controls (NC), energy and period content were studied for several common resting-state networks. Compared to STFT and MODWT, EMD showed the largest differences in energy and period between PD and NC subjects. Using a support vector machine, EMD achieved the highest prediction accuracy in classifying NC and PD subjects among STFT, MODWT, and EMD.
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