ArticleFrontiers in neuroscience2020
Is Brain Dynamics Preserved in the EEG After Automated Artifact Removal? A Validation of the Fingerprint Method and the Automatic Removal of Cardiac Interference Approach Based on Microstate Analysis.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 9 citations in OpenAlex.
- Classifying Obsessive-Compulsive Disorder from Resting-State EEG Using Convolutional Neural Networks: A Pilot Study.Computational psychiatry (Cambridge, Mass.) · 2026Article
- Classifying Obsessive-Compulsive Disorder from Resting-State EEG using Convolutional Neural Networks: A Pilot Study.medRxiv : the preprint server for health sciences · 2025Article
- Causal link between prefrontal cortex and EEG microstates: evidence from patients with prefrontal lesion.Frontiers in neuroscience · 2023Article
- Subject-Dependent Artifact Removal for Enhancing Motor Imagery Classifier Performance under Poor Skills.Sensors (Basel, Switzerland) · 2022Article
- Open Eyes Increase Neural Oscillation and Enhance Effective Brain Connectivity of the Default Mode Network: Resting-State Electroencephalogram Research.Frontiers in neuroscience · 2022Article
- Test-Retest Reliability of Synchrony and Metastability in Resting State fMRI.Brain sciences · 2021Article
- Automated Detection and Removal of Cardiac and Pulse Interferences from Neonatal EEG Signals.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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 (
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