# A hybrid analytical-numerical algorithm for determining the neuronal current via EEG

## TL;DR

This paper introduces a hybrid analytical-numerical method using radial basis functions and Tikhonov regularization to accurately reconstruct neuronal currents from EEG data, addressing the ill-posed inverse problem efficiently.

## Contribution

It presents a novel hybrid algorithm combining RBF expansion and regularization for neuronal current reconstruction from EEG, improving computational efficiency and accuracy.

## Key findings

- Reconstruction achieved RMSE of 0.1122 with synthetic data.
- Method is computationally efficient and suitable for MATLAB implementation.
- Effective in handling noisy EEG data with 20 dB SNR.

## Abstract

In this study, the neuronal current in the brain is represented using Helmholtz decomposition. It was shown in earlier work that data obtained via electroencephalography (EEG) are affected only by the irrotational component of the current. The irrotational component is denoted by $\Psi$ and has support in the cerebrum. This inverse problem is severely ill-posed and requires that additional constraints are imposed. Here, we impose the requirement of the minimization of the $L_2$ norm of the current (energy). The function $\Psi$ is expanded in terms of inverse multiquadric radial basis functions (RBF) on a uniform Cartesian grid inside the cerebrum. The minimal energy constraint in conjunction with the RBF parametrization of $\Psi$ results in a Tikhonov regularized solution of $\Psi$. The RBF shape parameter (regularization parameter), is computed by solving a 1-D non-linear maximization problem. Reconstructions are presented using synthetic data with a signal to noise ratio (SNR) of $20$ dB. The root mean square error (RMSE) between the exact and the reconstructed $\Psi$ is RMSE=$0.1122$. The proposed reconstruction algorithm is computationally efficient and can be vectorized in MATLAB.