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Fused multivariate empirical mode decomposition (MEMD) and inverse solution method for EEG source localization.

Pegah Khosropanah1, Abdul Rahman Ramli2, Kheng Seang Lim3

  • 1Department of Computer and Communication Systems Engineering, University Putra Malaysia, 43400 UPM-Serdang, Malaysia, Phone: +(60)182063014.

Biomedizinische Technik. Biomedical Engineering
|July 23, 2017
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Summary

This study presents a novel EEG source localization method combining multivariate empirical mode decomposition (MEMD) and sLORETA for accurate epileptogenic zone identification. The fused approach significantly reduces localization error, aiding pre-surgical evaluation.

Keywords:
BEMEEGMEMDepilepsy source localizationinverse solutionrealistic head model

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Scalp electroencephalography (EEG) is used for brain activity source localization, but accuracy is affected by data processing steps.
  • Accurate localization of the epileptogenic zone is crucial for pre-surgical evaluation in epilepsy patients.

Purpose of the Study:

  • To introduce a fused algorithm combining multivariate empirical mode decomposition (MEMD) and an inverse solution with an unsupervised eye blink remover for precise epileptogenic zone localization.
  • To evaluate the algorithm's accuracy using realistic forward models and compare it with established inverse methods.

Main Methods:

  • Construction of patient-specific forward models using MRI and the boundary element method (BEM).
  • Development of an unsupervised wavelet-based algorithm for eye blink artifact removal.
  • Application of MEMD for feature extraction from non-linear, non-stationary EEG signals, followed by localization using weighted minimum norm estimation (wMN) and standardized low resolution tomography (sLORETA).

Main Results:

  • The proposed fused algorithm demonstrated high agreement with MRI references, validated by a specialist.
  • Fusion of MEMD and sLORETA achieved near-zero spatial localization error compared to the MRI reference.
  • The algorithm's high accuracy with non-invasive, low-resolution EEG suggests clinical potential.

Conclusions:

  • The integrated MEMD and sLORETA approach with artifact removal offers a highly accurate method for EEG source localization.
  • This technique shows significant promise for improving pre-surgical evaluation and epileptogenic zone localization in clinical settings.