ArticleScientific reports2024
Optimizing EEG ICA decomposition with data cleaning in stationary and mobile experiments.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Neuroscientific Insights into the Built Environment: A Systematic Review of Empirical Research on Indoor Environmental Quality, Physiological Dynamics, and Psychological Well-Being in Real-Life Contexts.International journal of environmental research and public health · 2025Pooled it
- Preprocessing choices for P3 analyses with mobile EEG: A systematic literature review and interactive exploration.Psychophysiology · 2025Pooled it
- Electrocortical activity during resistance exercises in healthy young adults-a systematic review.Frontiers in sports and active living · 2024Pooled it
- From Traditional Tasks to Hyperscanning: Exploratory Evidence for Selective Modulation of Sensorimotor Neurodynamics Using Time-Resolved Generalized Additive Model Analysis.The European journal of neuroscience · 2026Article
- Reliable but Wrong? Automated Detection of Heart Rate Artifacts as a Source of Bias in Pediatric EEG Data.Research square · 2026Article
- SSDLabeler: realistic semi-synthetic data generation for multi-label artifact classification in EEG.Scientific reports · 2026Article
- Preprocessing on the Go: Practices in Gait-Related Mobile EEG.Psychophysiology · 2026Review
- Modulation of Visual Contrast Perception Associated With Dorsal Attention Network Connectivity Assessed by Magnetoencephalography.Human brain mapping · 2026Article
- Aberrant Beta-Band Network Alteration Preceding Freezing of Gait in Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2026Article
- Suppressing Non-Stationary Motion Artefacts in Mobile EEG Using Generalized Eigenvalue Decomposition.Sensors (Basel, Switzerland) · 2026Article
- Neurocognitive Dynamics of Translating Information From a Spatial Map Into Action.Psychophysiology · 2026Article
- Cortical dynamics are differentially associated with decision-making strategies in human wayfinding: An EEG study.Cognitive, affective & behavioral neuroscience · 2026Article
- Physiologically Guided Modeling for EEG Multichannel Signals.Bioengineering (Basel, Switzerland) · 2026Article
- Analysis of inter-brain synchrony in group-based electroencephalography to assess task-dependent interactions.Frontiers in neuroergonomics · 2026Article
- ReHA-Net: a ReVIN-hybrid attention network with multiscale convolution for robust EEG artifact removal in brain-computer interfaces.Scientific reports · 2025Article
- Exploring Brain Dynamics Within the Approach-Avoidance Bias.Brain sciences · 2025Article
- Theta Activity Supports Landmark-Based Correction of Naturalistic Human Path Integration.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Current Practices for Mental Fatigue Quantification and Induction in Movement Science: Introducing the SPeCIFY Guidelines.Sports medicine (Auckland, N.Z.) · 2025Review
- Where do I go? Decoding temporal neural dynamics of scene processing and visuospatial memory interactions using convolutional neural networks.Journal of vision · 2025Article
- Test-retest reliability of kinematic and EEG low-beta spectral features in a robot-based arm movement task.Biomedical physics & engineering express · 2025Article
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3 authors.
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Abstract
Electroencephalography (EEG) studies increasingly utilize more mobile experimental protocols, leading to more and stronger artifacts in the recorded data. Independent Component Analysis (ICA) is commonly used to remove these artifacts. It is standard practice to remove artifactual samples before ICA to improve the decomposition, for example using automatic tools such as the sample rejection option of the AMICA algorithm. However, the effects of movement intensity and the strength of automatic sample rejection on ICA decomposition have not been systematically evaluated. We conducted AMICA decompositions on eight open-access datasets with varying degrees of motion intensity using varying sample rejection criteria. We evaluated decomposition quality using mutual information of the components, the proportion of brain, muscle, and 'other' components, residual variance, and an exemplary signal-to-noise ratio. Within individual studies, increased movement significantly decreased decomposition quality, though this effect was not found across different studies. Cleaning strength significantly improved the decomposition, but the effect was smaller than expected. Our results suggest that the AMICA algorithm is robust even with limited data cleaning. Moderate cleaning, such as 5 to 10 iterations of the AMICA sample rejection, is likely to improve the decomposition of most datasets, regardless of motion intensity.
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