Sensitivity enhancement of task-evoked fMRI using ensemble empirical mode decomposition

Shang Hua N. Lin, Geng Hong Lin, Pei Jung Tsai, Ai Ling Hsu, Men Tzung Lo, Albert C. Yang, Ching Po Lin, Changwei W. Wu

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Background: Functional magnetic resonance imaging (fMRI) is widely used to investigate dynamic brain functions in neurological and psychological issues; however, high noise level limits its applicability for intensive and sophisticated investigations in the field of neuroscience. New method: To deal with both issue (low sensitivity and dynamic signal), we used ensemble empirical mode decomposition (EEMD), an adaptive data-driven analysis method for nonstationary and nonlinear features, to filter task-irrelevant noise from raw fMRI signals. Using both simulations and representative fMRI data, we optimized the analytic parameters and identified non-meaningful intrinsic mode functions (IMFs) to remove noise. Results: We revealed the following advantages of EEMD in fMRI analysis: (1) EEMD achieved high detectability for task engagement; (2) the functional sensitivity was markedly enhanced by removing task-irrelevant artifacts based on EEMD. Comparison with existing method(s): Compared with other noise-removal methods (e.g., band-pass filtering and independent component analysis), the EEMD-based artifact-removal method exhibited better spatial specificity and superior Gaussianity of the resulting t-score distribution. Conclusions: We found that EEMD method was efficient to enhance the functional sensitivity of evoked fMRI. The same strategy would be applicable to resting-state fMRI signal in the general purpose.

Original languageEnglish
Pages (from-to)56-66
Number of pages11
JournalJournal of Neuroscience Methods
StatePublished - 30 Jan 2016


  • Ensemble empirical mode decomposition (EEMD)
  • FMRI sensitivity
  • Hilbert-Huang transform
  • Nonlinear
  • Nonstationary
  • Resting-state fMRI
  • Task-fMRI


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