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The SESAMEEG package: a probabilistic tool for source localization and uncertainty quantification in M/EEG.

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Summary

The SESAME (SEquential SemiAnalytic Montecarlo Estimator) algorithm accurately localizes neural sources from M/EEG data, quantifying uncertainty and offering flexibility for epilepsy pre-surgical evaluations.

Keywords:
Bayesian inferenceEEGMATLABMEGPythoninverse problemsopen-source software

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Computational Neuroscience

Background:

  • Magnetoencephalography (M/EEG) data analysis relies heavily on accurate source localization.
  • Clinical applications, such as epilepsy pre-surgical evaluation, demand robust source localization methods.
  • Existing methods may have limitations in accuracy, parameter sensitivity, or uncertainty quantification.

Purpose of the Study:

  • To introduce and describe the SESAME (SEquential SemiAnalytic Montecarlo Estimator) algorithm for M/EEG source localization.
  • To detail the comprehensive output of the SESAME algorithm and its practical applications.
  • To provide a user-friendly guide to SESAME implementation and interpretation.

Main Methods:

  • Bayesian source localization using the SESAME algorithm.
  • Sequential SemiAnalytic Montecarlo estimation.
  • Frequency domain analysis for neural oscillations.

Main Results:

  • SESAME demonstrates high accuracy in localizing focal neural sources.
  • The method quantifies the uncertainty associated with source reconstruction.
  • SESAME is robust to input parameter variations and accepts user-defined search regions.
  • The algorithm can identify generators of neural oscillations in the frequency domain.

Conclusions:

  • SESAME offers a powerful and accurate Bayesian approach to M/EEG source localization.
  • Its flexibility, uncertainty quantification, and open-source availability (SESAMEEG) make it valuable for research and clinical applications.
  • A thorough understanding of SESAME's output is crucial for effective utilization in M/EEG analysis pipelines.