ArticleNeuroImage2018
Applying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition.
Article in NeuroImage, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
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Who cites it
54 citing papers in PubMed.
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- Characterization of multi-channel intraneural stimulation in transradial amputees.Scientific reports · 2019Trial
- Task-Related Aperiodic EEG (1/f) Activity in Autism.Autism research : official journal of the International Society for Autism Research · 2026Article
- Electrophysiological Changes in Resting-State EEG Following REAC BWO-G_B Neurobiological Modulation in Healthy Adults: A Spectral and Multivariate Exploratory Study.Brain sciences · 2026Article
- Enhanced cervical cancer diagnosis using a novel Bayesian fusion ensemble method with explainable AI.Scientific reports · 2026Article
- Machine learning-based lateralization and localization of seizure onset in focal cortical dysplasia patients using ictal scalp EEG.Frontiers in neuroscience · 2026Article
- Auditory paradigm-dependent EEG feature screening and representation for distinguishing consciousness states in male patients with disorders of consciousness.Frontiers in neurology · 2026Article
- Sensory Entrained TMS (seTMS) enhances motor cortex plasticity.bioRxiv : the preprint server for biology · 2025Article
- Sensory Entrained TMS (seTMS) Enhances Motor Cortex Excitability.Human brain mapping · 2025Article
- Using principal component analysis to determine which vestibular stimuli provide best biomarkers for separating Alzheimer's from mixed Alzheimer's disease.Medical & biological engineering & computing · 2025Article
- Machine learning for workpiece mass prediction using real and synthetic acoustic data.Scientific reports · 2025Article
- How does Independent Component Analysis Preprocessing Affect EEG Microstates?Brain topography · 2025Article
- Denoising strategies of functional connectivity MRI data in lesional and non-lesional brain diseases.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Different sustained and induced alpha oscillations emerge in the human auditory cortex during sound processing.Communications biology · 2024Article
- Optimizing EEG Signal Integrity: A Comprehensive Guide to Ocular Artifact Correction.Bioengineering (Basel, Switzerland) · 2024Article
- Identification of Early Hippocampal Dynamics during Recognition Memory with Independent Component Analysis.eNeuro · 2024Article
- A machine learning approach for real-time cortical state estimation.Journal of neural engineering · 2024Article
- Exploring Convolutional Neural Network Architectures for EEG Feature Extraction.Sensors (Basel, Switzerland) · 2024Article
- Prediction model of land surface settlement deformation based on improved LSTM method: CEEMDAN-ICA-AM-LSTM (CIAL) prediction model.PloS one · 2024Article
Corrections and comments
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3 authors.
Funding
Abstract
Independent Component Analysis (ICA) has proven to be an effective data driven method for analyzing EEG data, separating signals from temporally and functionally independent brain and non-brain source processes and thereby increasing their definition. Dimension reduction by Principal Component Analysis (PCA) has often been recommended before ICA decomposition of EEG data, both to minimize the amount of required data and computation time. Here we compared ICA decompositions of fourteen 72-channel single subject EEG data sets obtained (i) after applying preliminary dimension reduction by PCA, (ii) after applying no such dimension reduction, or else (iii) applying PCA only. Reducing the data rank by PCA (even to remove only 1% of data variance) adversely affected both the numbers of dipolar independent components (ICs) and their stability under repeated decomposition. For example, decomposing a principal subspace retaining 95% of original data variance reduced the mean number of recovered 'dipolar' ICs from 30 to 10 per data set and reduced median IC stability from 90% to 76%. PCA rank reduction also decreased the numbers of near-equivalent ICs across subjects. For instance, decomposing a principal subspace retaining 95% of data variance reduced the number of subjects represented in an IC cluster accounting for frontal midline theta activity from 11 to 5. PCA rank reduction also increased uncertainty in the equivalent dipole positions and spectra of the IC brain effective sources. These results suggest that when applying ICA decomposition to EEG data, PCA rank reduction should best be avoided.
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