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Cortical Source Analysis of High-Density EEG Recordings in Children
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Low-Density EEG for Neural Activity Reconstruction Using Multivariate Empirical Mode Decomposition.

Andres Soler1, Pablo A Muñoz-Gutiérrez2,3, Maximiliano Bueno-López4

  • 1Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, Norway.

Frontiers in Neuroscience
|March 18, 2020
PubMed
Summary

Multivariate Empirical Mode Decomposition (MEMD) improves electroencephalography (EEG) neural activity reconstruction by reducing mode-mixing. This method enhances source estimation accuracy, especially with low-density EEG electrode montages.

Keywords:
EEG signalsbrain mappinglow-density EEGmultivariate empirical mode decompositionneuronal activity reconstructiontime-frequency decomposition

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Area of Science:

  • Neuroscience
  • Signal Processing
  • Biomedical Engineering

Background:

  • Estimating neural activity from electroencephalography (EEG) is crucial for understanding brain function.
  • Empirical Mode Decomposition (EMD) offers time-frequency insights but suffers from mode-mixing, hindering optimal EEG reconstruction.
  • Existing studies often use single-channel analysis or multi-channel approaches for different applications.

Purpose of the Study:

  • To investigate the efficacy of Multivariate Empirical Mode Decomposition (MEMD) for improving EEG-based neural activity reconstruction.
  • To reduce the mode-mixing problem inherent in EMD when applied to multi-channel EEG data.
  • To provide effective a priori time-frequency information for neuronal activity reconstruction using low-density EEG electrode montages.

Main Methods:

  • Employed Multivariate Empirical Mode Decomposition (MEMD) for multi-channel EEG analysis.
  • Utilized the Multiple Sparse Priors (MSP) algorithm for source reconstruction.
  • Evaluated performance using real and synthetic EEG data with 32, 16, and 8 electrode montages.
  • Assessed reconstruction quality using the Wasserstein metric.

Main Results:

  • MEMD pre-processing significantly improved source reconstruction accuracy compared to no pre-processing, particularly for 8 and 16 electrode montages.
  • Mean source reconstruction error was reduced by 59.42% (8 electrodes) and 66.04% (16 electrodes) on real EEG data.
  • On simulated data, MEMD reduced error by 87.31% (8 electrodes) and 31.45% (16 electrodes).

Conclusions:

  • MEMD effectively mitigates the mode-mixing problem in EEG analysis.
  • MEMD provides valuable a priori information, enhancing neural activity reconstruction with low-density EEG montages.
  • This approach offers a promising method for more accurate neural source estimation in EEG studies.