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EECoG-Comp: An Open Source Platform for Concurrent EEG/ECoG Comparisons-Applications to Connectivity Studies.

Qing Wang1,2, Pedro Antonio Valdés-Hernández3, Deirel Paz-Linares1

  • 1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.

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Summary
This summary is machine-generated.

Electrophysiological Source Imaging (ESI) validation is challenging. A new open-source platform, EECoG-Comp, addresses artifacts and modeling issues, but current ESI methods show limited accuracy in connectivity estimation.

Keywords:
ConnectivityECoGEEGElectrophysiological source imagingForward modelingMonkey electrophysiologyOpen science

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Computational Biology

Background:

  • Electrophysiological Source Imaging (ESI) lacks gold standards for validation.
  • Concurrent electroencephalography (EEG) and electrocorticography (ECoG) experiments (EECoG) offer a solution, particularly in primate models.
  • Existing public EECoG data is limited, hindering research.

Purpose of the Study:

  • To develop specialized procedures for processing EECoG data, including artifact handling, sophisticated forward modeling, and reliable statistical analysis for source connectivity.
  • To introduce EECoG-Comp, an open-source platform addressing these processing challenges.
  • To evaluate the performance of popular ESI methods using the Multidimensional Recording (MDR) dataset.

Main Methods:

  • Developed EECoG-Comp, an open-source platform for EECoG data processing.
  • Utilized Finite Element Method (FEM) for calculating EECoG lead fields.
  • Applied four ESI methods (MNE, LCMV, eLORETA, SSBL) and ECoG Laplacian to real and simulated data.

Main Results:

  • Real MDR data showed moderate concordance between ESI methods and ECoG Laplacian.
  • Simulations in both monkey and human models revealed poor correspondence between ESI/Laplacian methods and true source connectivity.
  • The developed platform facilitates the analysis of complex EECoG data.

Conclusions:

  • Current ESI methods demonstrate limitations in accurately estimating neural connectivity.
  • The EECoG-Comp platform provides essential tools for EECoG data analysis.
  • Further development of ESI connectivity estimators and the generation of more EECoG datasets are crucial for neurobiologically valid inferences.