Abstract
The assessment of a method for removing artifacts from electroencephalography (EEG) datasets often disregard verifying that global brain dynamics is preserved. 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. 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. 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. 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). No statistically significant differences between the expert and the automated classification of the artifactual ICs were found (p > 0.05). Cronbach’s α values assessed the high test–retest reliability of microstate parameters for EEG datasets denoised by the automated procedure. 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. 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 < 0.01). Descriptive statistics and Cronbach’s α of microstate metrics highlighted no significant differences and excellent consistency between groups (p > 0.5). 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. 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.
Highlights
IntroductionThe 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-ofthe-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)
The p-values were always much greater than 0.05, indicating that no significant differences between the expert IC classification performed by visual inspection and the IC classification performed with the optimized fingerprint method and the automatic removal of cardiac interference (ARCI) approach could be observed
Similarity of Global Microstate Templates Across Groups The similarity between pairs of global microstate templates from the EEG datasets denoised by expert classification and by automated classification was assessed by means of global dissimilarity (GD) separately for the two rest conditions (DNEEG1-EO vs. DNEEG2-EO and DNEEG1-EC vs. DNEEG2EC)
Summary
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-ofthe-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). Clear reference signals cannot be used for some sources of noise such as myogenic activity or noise from external instrumentation, whereas in some newer applications of EEG, such as in sports sciences applications (Stone et al, 2019), the acquisition of reference signals simultaneously with EEG is often problematic. In these cases, to use regression methods or adaptive filtering for artifact removal could become impossible
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