Niranjana Sudheer | Searcharxiv

arXiv2025

Weighted Group Lasso for a static EEG problem

We investigate the weighted Group Lasso formulation for the static inverse electroencephalography (EEG) problem, aiming at reconstructing the unknown underlying neuronal sources from voltage measurements on the scalp. By modelling the three orthogonal dipole components at each location as a single coherent group, we demonstrate that depth bias and orientation bias can be effectively mitigated through the proposed regularization framework. On the theoretical front, we provide concise recovery guarantees for both single and multiple group sources. Our numerical experiments highlight that while theoretical bounds hold for a broad range of weight definitions, the practical reconstruction quality, for cases not covered by the theory, depends significantly on the specific weighting strategy employed. Specifically, employing a truncated Moore-Penrose pseudoinverse for the involved weighting matrix gives a small Dipole Localization Error (DLE). The proposed method offers a robust approach for inverse EEG problems, enabling improved spatial accuracy and a more physiologically realistic reconstruction of neural activity.

math.NA↗

arXiv2025

Weighting operators for sparsity regularization

Standard regularization methods typically favor solutions which are in, or close to, the orthogonal complement of the null space of the forward operator/matrix $\mathsf{A}$. This particular biasedness might not be desirable in applications and can lead to severe challenges when $\mathsf{A}$ is non-injective. We have therefore, in a series of papers, investigated how to "remedy" this fact, relative to a chosen basis and in a certain mathematical sense: Based on a weighting procedure, it turns out that it is possible to modify both Tikhonov and sparsity regularization such that each member of the chosen basis can be almost perfectly recovered from their image under $\mathsf{A}$. In particular, we have studied this problem for the task of using boundary data to identify the source term in an elliptic PDE. However, this weighting procedure involves $\mathsf{A}^\dagger \mathsf{A}$, where $\mathsf{A}^\dagger$ denotes the pseudo inverse of $\mathsf{A}$, and can thus be CPU-demanding and lead to undesirable error amplification. We therefore, in this paper, study alternative weighting approaches and prove that some of the recovery results established for the methodology involving $\mathsf{A}$ hold for a broader class of weighting schemes. In fact, it turns out that "any" linear operator $\mathsf{B}$ has an associated proper weighting defined in terms of images under $\mathsf{B}\mathsf{A}$. We also present a series of numerical experiments, employing different choices of $\mathsf{B}$.

math.NA↗