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Empirical Mode Decomposition is a technique for evaluating information that is neither steady nor periodic. Unlike standard approaches, EMD paired with the Hilbert transform eliminates the uncertainty principle from time-frequency analysis. This research investigates the use of EMD for processing and analyzing EEG data, providing a thorough introduction of the EMD methodology. EMD outperforms Fourier and wavelet approaches, particularly in identifying Event Related Synchronization and DE synchronization, as well as intra-wave frequency modulation in the alpha band. We evaluate the compensations and shortcomings of various EMD-based techniques and provide notable instances of EMD usage in EEG analysis.

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A Review of Empirical Mode Decomposition in EEG Time-Frequency Analysis

  • 会议论文

  • 发表日期：

    2026年

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  • Shital R. Shegokar,
  • Vivek Upadhyay

摘要

Empirical Mode Decomposition is a technique for evaluating information that is neither steady nor periodic. Unlike standard approaches, EMD paired with the Hilbert transform eliminates the uncertainty principle from time-frequency analysis. This research investigates the use of EMD for processing and analyzing EEG data, providing a thorough introduction of the EMD methodology. EMD outperforms Fourier and wavelet approaches, particularly in identifying Event Related Synchronization and DE synchronization, as well as intra-wave frequency modulation in the alpha band. We evaluate the compensations and shortcomings of various EMD-based techniques and provide notable instances of EMD usage in EEG analysis.