Using reciprocity for relating the simulation of transcranial current stimulation to the EEG forward problem

Area of Science:

• Computational Neuroscience
• Biomedical Engineering
• Electrophysiology

Background:

• The electroencephalography (EEG) forward problem models electrical potentials on the scalp from brain activity.
• Transcranial current stimulation (tCS) uses electrical fields to modulate brain activity, requiring accurate modeling.
• Finite element method (FEM) is a common numerical technique for simulating these bioelectrical phenomena.

Purpose of the Study:

• To investigate the relationship between EEG and tCS forward problems using FEM.
• To compare the accuracy and efficiency of different FEM-based approaches for EEG and tCS.
• To validate FEM-based tCS simulations using analytical EEG solutions.

Main Methods:

• Compared adjoint approach (AA) and partial integration (PI) with a transfer matrix concept for EEG forward problem.
• Utilized spherical shell models and analytically derived EEG potentials for validation.
• Evaluated FEM approaches for tCS simulation using regular and geometry-adapted hexahedral elements, and surface-segmentation based tetrahedral FEM.

Main Results:

• AA and PI methods for EEG forward problem are algebraically identical, establishing a link to tCS.
• Quasi-analytical EEG solutions validate FEM-based tCS simulations.
• Geometry-adapted hexahedral FEM achieves high accuracy comparable to tetrahedral FEM, with significant improvements in tCS current flow simulation compared to regular hexahedral FEM.

Conclusions:

• The established link between EEG and tCS forward problems simplifies modeling and validation.
• Geometry-adapted hexahedral FEM offers a practical and accurate method for realistic tCS modeling.
• Accurate tCS modeling is crucial, as FEM approach significantly impacts current flow predictions.