Combining spatial wavelets and sparse Bayesian learning for extended brain sources reconstruction - Institut de Neurosciences des Systèmes Access content directly
Preprints, Working Papers, ... Year : 2024

Combining spatial wavelets and sparse Bayesian learning for extended brain sources reconstruction

Abstract

Objective: the accurate reconstruction of extended cortical activity from M/EEG data is a difficult, ill-conditioned problem. This work proposes to model unknown sources as expansions on a wavelet system defined on the cortical surface, and addresses resulting numerical optimization problems. The objective is to obtain accurate source localization, together with quantitatively relevant amplitude and time course. Approach: Unknown sources are expanded on a system of spectral graph wavelets (SGW) defined on the cortical surface. Unknown wavelet coefficients are estimated using either variational or Bayesian formulations, involving priors that favor extended source through sparsity in the wavelet domain: sparsity- inducing regularization, or sparse Bayesian learning (SBL). These approaches are tested and compared with concurrent approaches on numerical simulations. The quality of reconstructions is assessed using a set of complementary metrics. Results: SGW-based approaches are able to identify accurately extended sources. The combination with SBL is particularly attractive, as it doesn’t involve hyperparameter tuning. It yields accurate and robust results with respect to all considered metrics, and performs remarkably well in terms of depth bias. Conclusion: This paper demonstrates the usefulness of cortical wavelets for reconstructing cortical activity from M/EEG data, and the impact of sparse Bayesian learning in this context. Significance: Being able to identify localization, depth and time course of brain activity from M/EEG data is important in clinical applications such as epilepsy, as it can improve the detection of potential sources of seizures.
Fichier principal
Vignette du fichier
main.pdf (1.03 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-04558288 , version 1 (24-04-2024)

Licence

Identifiers

  • HAL Id : hal-04558288 , version 1

Cite

Samy Mokhtari, Jean-Michel Badier, Christian G. Bénar, Bruno Torrésani. Combining spatial wavelets and sparse Bayesian learning for extended brain sources reconstruction. 2024. ⟨hal-04558288⟩
16 View
29 Download

Share

Gmail Mastodon Facebook X LinkedIn More