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FEMfuns: A Volume Conduction Modeling Pipeline that Includes Resistive, Capacitive or Dispersive Tissue and

M Vermaas¹, M C Piastra², T F Oostendorp²

¹Department of Neuroinformatics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, the Netherlands. m.vermaas@science.ru.nl.

Neuroinformatics

|April 20, 2020

Summary

A new open-source pipeline, FEMfuns, enables precise simulation of electrocorticography (ECoG) signals. This tool optimizes electrode placement and properties for advanced brain-computer interfaces by accurately modeling neuronal activity.

Keywords:

Complete electrode model Computational modeling Dispersive tissue Electrical double layer Finite element method

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Area of Science:

Computational neuroscience
Biomedical engineering
Electrophysiology

Background:

High-precision neuronal recordings are crucial for brain-computer interfaces (BCIs).
Optimizing electrocorticography (ECoG) grid placement and electrode properties is essential for signal quality.
Accurate simulation of the ECoG forward problem, incorporating electrode characteristics, is needed.

Purpose of the Study:

Introduce FEMfuns, the first open-source pipeline for solving the ECoG forward problem using the finite element method (FEM).
Provide a versatile tool for neuroscientists to simulate extracellular potentials with various geometrical domains, source models, and electrode properties.
Enable optimization of ECoG grids and exploration of novel electrode materials for BCI applications.

Main Methods:

Developed FEMfuns based on the finite element method (FEM) implemented in FEniCS.
Pipeline includes geometry tessellation, multiple electrode-electrolyte interface models, and adaptive refinement.
Tested with diverse models, including a multi-layer sphere and a realistic head model, using dipolar and other source configurations.

Main Results:

FEMfuns accurately approximates analytical solutions with fine mesh tessellation.
Demonstrated dispersive material and interface effects, particularly capacitive effects at the electrode-electrolyte interface during stimulation.
Validated the pipeline's flexibility and accuracy in simulating ECoG signals.

Conclusions:

FEMfuns is an accurate and flexible open-source tool for simulating ECoG signals.
The pipeline facilitates the optimization of electrode grids and materials for advanced BCIs.
Accurate modeling of electrode properties and interface effects is critical for BCI development.