Quantitative Evaluation of Artifact Removal in Real Magnetoencephalogram Signals with Blind Source Separation

Abstract

The Magnetoencephalogram (MEG) is contaminated with undesired signals, which are called artifacts. Some of the most important ones are the cardiac and the ocular artifacts (CA and OA, respectively), and the power line noise (PLN).Blind Source Separation (BSS) has been used to reduce the influence of the artifacts in the data. There is a plethora of BSS-based artifact removal approaches, but few comparative analyses. In this study, MEG background activity from 26 subjects was processed with five widespread BSS (AMUSE, SOBI, JADE, extended Infomax, and FastICA) and one Constrained BSS (cBSS) techniques. Then, the ability of several combinations of BSS algorithm, epoch length, and artifact detection metric to automatically reduce the CA, OA, and PLN were quantified with objective criteria. The results pinpointed to cBSS as a very suitable approach to remove the

Altered cross-frequency coupling in resting-state MEG after mild traumatic brain injury

“…Filtering with a notch filter at 60 Hz was used to reduce the effects of line noise and it was followed by independent component analysis (ICA) to separate cerebral from non-cerebral activity using the extended Infomax algorithm as implemented in EEGLAB (Delorme and Makeig, 2004). The data were also whitened and reduced in dimensionality using principal component analysis with a threshold set to 95% of the total variance (Delorme and Makeig, 2004;Escudero et al, 2011;Antonakakis et al, 2013). The statistical values of kurtosis, Rényi entropy, and skewness of each independent component were used to eliminate ocular and cardiac artifacts.…”

“…The statistical values of kurtosis, Rényi entropy, and skewness of each independent component were used to eliminate ocular and cardiac artifacts. A component was considered an artifact if more than 20% of its values after normalization to zero mean and unit variance were outside the range [-2, +2] (Escudero et al, 2011;Dimitriadis et al, 2013a;Antonakakis et al, 2013Antonakakis et al, , 2015.…”

Detection of Artifacts and Brain Responses Using Instantaneous Phase Statistics in Independent Components

“…Independent component analysis (ICA), for example, is widely used to separate brain signals from noise and artifact components. Effective removal of noise and biological artifacts was reported in various publications (Dammers et al, 2010;Escudero et al, 2011;Mantini et al, 2008;Nikulin et al, 2011;Ting et al, 2006). Most of the proposed ICA based source separation methods are performed to identify and exclude artifacts from the recorded signal, while only a few studies have been performed to elicit brain responses from the decomposed signals (Dammers et al, 2010;Hild & Nagarajan, 2009;Nikulin et al, 2011).…”

“…Independent component analysis (ICA) is widely used to separate brain signals from artifact components using utilizing both semi-and fully automated procedures (Dammers et al, 2008;Escudero, et al, 2010Escudero, et al, , 2011Hironaga & Ioannides, 2007;Li, et al, 2006;Mantini et al, 2008;Ossadtchi et al, 2004;Ting et al, 2006). Independent component analysis was developed for solving the blind source separation (BSS) problem with the basic assumption, that the recorded data in a sensor array are linear sums of temporally independent components originating from spatially fixed sources (Bell & Sejnowski, 1995;Comon, 1994;Herault & Jutten, 1986;Hyvärinen, 1999).…”

“…After signal decomposition the problem now is to objectively identify components of interest in such high dimensional data sets, as it is the case for modern MEG systems. Different strategies have been proposed to exactly tackle the problem of unsupervised component extraction to find either artifacts (Dammers et al, 2008;Escudero et al, 2011;Li et al, 2006;Mantini et al, 2008) or signal of interest (Dammers et al, 2010;Hild & Nagarajan, 2009;Nikulin et al, 2011;Vairavan et al, 2010). Apart from time series analysis applied to the independent components for the identification of the source of interest, it might be helpful to look at the so-called place map of each temporally independent component.…”