ArticleNeuroImage2024
Assessing the effectiveness of spatial PCA on SVM-based decoding of EEG data.
Article in NeuroImage, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Evaluating the effects of regularization and cross-validation parameters on the performance of SVM-based decoding of EEG data.Cortex; a journal devoted to the study of the nervous system and behavior · 2026Article
- Evaluating the effects of regularization and cross-validation parameters on the performance of SVM-based decoding of EEG data.bioRxiv : the preprint server for biology · 2026Article
- Experience-dependent Changes in the Visual Processing of Letters: Evidence from Electroencephalography Decoding.Journal of cognitive neuroscience · 2026Article
- Article
- A comprehensive EEG dataset and performance assessment for Autism Spectrum Disorder.Scientific reports · 2025Article
- Electroencephalographic (EEG) analysis of hue perception differences between art and non-art majors: insights from the P2 and P3 components.BMC psychology · 2025Article
- Assessing the impact of artifact correction and artifact rejection on the performance of SVM- and LDA-based decoding of EEG signals.NeuroImage · 2025Article
- Assessing the impact of artifact correction and artifact rejection on the performance of SVM-based decoding of EEG signals.bioRxiv : the preprint server for biology · 2025Article
- Automatic Detection and Classification of Natural Weld Defects Using Alternating Magneto-Optical Imaging and ResNet50.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
Principal component analysis (PCA) has been widely employed for dimensionality reduction prior to multivariate pattern classification (decoding) in EEG research. The goal of the present study was to provide an evaluation of the effectiveness of PCA on decoding accuracy (using support vector machines) across a broad range of experimental paradigms. We evaluated several different PCA variations, including group-based and subject-based component decomposition and the application of Varimax rotation or no rotation. We also varied the numbers of PCs that were retained for the decoding analysis. We evaluated the resulting decoding accuracy for seven common event-related potential components (N170, mismatch negativity, N2pc, P3b, N400, lateralized readiness potential, and error-related negativity). We also examined more challenging decoding tasks, including decoding of face identity, facial expression, stimulus location, and stimulus orientation. The datasets also varied in the number and density of electrode sites. Our findings indicated that none of the PCA approaches consistently improved decoding performance related to no PCA, and the application of PCA frequently reduced decoding performance. Researchers should therefore be cautious about using PCA prior to decoding EEG data from similar experimental paradigms, populations, and recording setups.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.