A Novel Bayesian Approach for EEG Source Localization

Abstract

We propose a new method for EEG source localization. An efficient solution to this problem requires choosing an appropriate regularization term in order to constraint the original problem. In our work, we adopt the Bayesian framework to place constraints; hence, the regularization term is closely connected to the prior distribution. More specifically, we propose a new sparse prior for the localization of EEG sources. The proposed prior distribution has sparse properties favoring focal EEG sources. In order to obtain an efficient algorithm, we use the variational Bayesian (VB) framework which provides us with a tractable iterative algorithm of closed-form equations. Additionally, we provide extensions of our method in cases where we observe group structures and spatially extended EEG sources. We have performed experiments using synthetic EEG data and real EEG data from three publicly available datasets. The real EEG data are produced due to the presentation of auditory and visual stimulus. We compare the proposed method with well-known approaches of EEG source localization and the results have shown that our method presents state-of-the-art performance, especially in cases where we expect few activated brain regions. The proposed method can effectively detect EEG sources in various circumstances. Overall, the proposed sparse prior for EEG source localization results in more accurate localization of EEG sources than state-of-the-art approaches.

Bayesian Inference for Brain Source Imaging with Joint Estimation of Structured Low-rank Noise

“…Other variants of MNE include the weighted MNE (wMNE) [5], low resolution brain electromagnetic tomography (LORETA) [6], and standardized LORETA (sLORETA) [7], etc. Another broad class of approach is to consider Bayesian techniques [8, 9, 10, 11, 12, 13, 14] with appropriate priors assigned to the model parameters. Recently introduced Champagne algorithm [8], a novel tomographic source reconstruction algorithm derived in an empirical Bayesian fashion with incorporation of deep theoretical ideas about sparse-source recovery from noisy, constrained measurements.…”