Estimation of M/EEG Non-stationary Brain Activity Using Spatio-temporal Sparse Constraints

A Bayesian framework for unifying data cleaning, source separation and imaging of electroencephalographic signals

“…To cope with the ill-posed nature of the inverse problem and ensure functional images with biological relevance, several inverse algorithms have been proposed that seek to estimate EEG sources subject to neurophysiologically reasonable spatial Friston et al, 2008;Trujillo-Barreto et al, 2004;Pascual-Marqui et al, 2002;Baillet et al, 2001;Hämäläinen and Ilmoniemi, 1994), spatiotemporal (Martínez-Vargas et al, 2015;Valdés-Sosa et al, 2009;Trujillo-Barreto et al, 2008), and frequency-domain (Gramfort et al, 2013) constraints, just to mention a few examples. These approaches can work relatively well when the EEG samples are corrupted by Gaussian noise and the signal to noise ratio (SNR) is high.…”

Automatic Selection of Frequency Bands for Electroencephalographic Source Localization

“…The main drawback of ESL is to solve the neuromagnetic inverse problem which is ill-posed and it does not have an unique solution. Therefore, to obtain an approximated locations of neural current sources from EEG, it is necessary to solve the inverse problem using some a priori information or applying some constraints over the source space [1], [2]. Nowadays, spatio-temporal constraints have been used in different works, in [1] was included, to improve the spatial resolution, a basis set for smoothing the source space (localized areas that could be potentially active brain regions) and based on a Markovian assumption applied at each sample time to estimate the brain activity, the time resolution was improved.…”

“…Nowadays, spatio-temporal constraints have been used in different works, in [1] was included, to improve the spatial resolution, a basis set for smoothing the source space (localized areas that could be potentially active brain regions) and based on a Markovian assumption applied at each sample time to estimate the brain activity, the time resolution was improved. Another spatio-temporal constraints were incorporated as a small and locally patches to reconstruct sparse brain activity; to smooth the solution over the time, temporal constraint was imposed for penalizing the difference between consecutive time points [2].…”