Robust and sparse estimator for EEG source localization

EEG source localization involves reconstructing brain activity from observed EEG measurements, a critical task for diagnosing various neurological disorders. The distributed approach to this problem is inherently ill-posed, posing significant challenges. In this study, we present a sparsity-controlled Lorentzian norm-based method for EEG source localization. This approach effectively balances robustness to measurement noise and sparsity in the solution.The proposed method employs a non-linear conjugate gradient descent algorithm to minimize the loss function, where the Lorentzian norm replaces the conventional ℓ2 norm. The Lorentzian norm’s unique ability to handle impulsive noise ensures precise estimation of active sources, even under challenging conditions. Comparative analyses with ℓ2, ℓ1 and ℓp,p<1 norm-based methods highlight the Lorentzian norm’s superior robustness and sparsity control. The results demonstrate that this novel approach improves the accuracy and reliability of EEG source localization, making it a valuable tool for medical applications.