FEMfuns

FEMfuns simulates extracellular potentials using the finite element method to solve the forward problem for electrocorticography (ECoG) and to optimize cortical electrode grid placement and electrode properties for brain–computer interface (BCI) applications.

Key Features:

• Geometrical Flexibility: Supports simulations across various geometrical domains, including multi-layer sphere models and realistically-shaped head models, and accommodates different source models and electrode configurations.
• Electrode Property Integration: Explicitly models electrode properties including resistive and capacitive behavior and multiple electrode–electrolyte implementations.
• Dispersive Modeling: Simulates dispersive tissue and electrode behavior to capture capacitive and dispersive effects at the electrode–electrolyte interface.
• Adaptive Refinement and Tessellation: Provides adaptive mesh refinement and geometry tessellation options to enhance spatial accuracy of FEM solutions.
• FEM Implementation: Implements forward-problem solvers using the FEniCS platform within a Python codebase.

Scientific Applications:

• BCI Electrode Optimization: Optimize electrode grid placement and electrode material/properties for ECoG recordings in brain–computer interfaces.
• Source Configuration Studies: Simulate dipolar and other source configurations in multi-layer sphere models and realistically-shaped head models to study signal propagation.
• Electrode Material Evaluation: Assess how resistive, capacitive, and dispersive electrode materials influence recorded signals and stimulation outcomes.
• Neuronal Population Recording Precision: Support analyses aimed at improving the precision of neuronal population activity recordings.

Methodology:

Solves the forward problem using the finite element method implemented in FEniCS; includes geometry tessellation, multiple electrode–electrolyte implementations, and adaptive refinement; provided as a Python codebase.