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The investigation of the human brain function largely relies on electroencephalography (EEG), a technique characterized by an excellent temporal resolution ( Niedermeyer and da Silva, 2005) that has recently undergone several technological advances in the electronics and sensor components to enable continuous, out-of-the-lab, and mobile EEG acquisitions ( Thompson et al., 2008 Del Percio et al., 2011 De Vos et al., 2011 Lance et al., 2012 Askamp and van Putten, 2014 Liao et al., 2014 Lopez-Gordo et al., 2014 Comani et al., 2015 Fiedler et al., 2015 Michel et al., 2015 di Fronso et al., 2016, 2019 Filho et al., 2016). They also suggest that microstate analysis could represent a novel approach for assessing the ability of an EEG denoising method to remove artifacts without altering brain dynamics. These results confirm the ability of the optimized fingerprint method and the ARCI approach to effectively remove physiological artifacts from EEG recordings while preserving global brain dynamics. For the differently denoised EEG datasets in the two recording conditions, we found that the global microstate templates and the sequences of global microstates were very similar ( p 0.5). The total EEG signal variance explained by the sets of global microstate templates was about 80% for all denoised EEG datasets, with no significant differences between groups. Cronbach’s α values assessed the high test–retest reliability of microstate parameters for EEG datasets denoised by the automated procedure. No statistically significant differences between the expert and the automated classification of the artifactual ICs were found ( p > 0.05). Brain dynamics in two different groups of denoised EEG signals, reconstructed after having removed the artifactual ICs identified by either visual inspection or the automated methods, was assessed by calculating microstate topographies, microstate metrics (duration, occurrence, and coverage), and directional predominance (based on transition probabilities).
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After signal decomposition by independent component analysis (ICA), the independent components (ICs) related to eyeblinks, eye movements, myogenic interference, and cardiac electromechanical activity were identified with the optimized fingerprint method and ARCI approach and statistically compared with the outcome of the expert classification of the ICs by visual inspection. We recorded EEG activity with a high-resolution EEG system during two resting-state conditions (eyes open, 25 volunteers, and eyes closed, 26 volunteers) known to exhibit different brain dynamics. In this study, we verified that the recently introduced optimized fingerprint method and the automatic removal of cardiac interference (ARCI) approach not only remove physiological artifacts from EEG recordings but also preserve global brain dynamics, as assessed with a new approach based on microstate analysis.
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The assessment of a method for removing artifacts from electroencephalography (EEG) datasets often disregard verifying that global brain dynamics is preserved. d’Annunzio” of Chieti-Pescara, Chieti, Italy