ArticleDevelopmental cognitive neuroscience2022
A practical introduction to EEG Time-Frequency Principal Components Analysis (TF-PCA).
Article in Developmental cognitive neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Functional and effective EEG connectivity patterns in Alzheimer's disease and mild cognitive impairment: a systematic review.Frontiers in aging neuroscience · 2025Pooled it
- Altered temporal organization of neural response dynamics during attention processing differentiates ADHD subtypes in children.NeuroImage. Clinical · 2026Article
- Posttraumatic reexperiencing and alcohol use: Mediofrontal theta as a neural mechanism for negative reinforcement.Journal of psychopathology and clinical science · 2025Article
- The association between sugar-sweetened beverage consumption, muscle strength, and psychological symptoms among Chinese adolescents: a multicenter cross-sectional survey.Frontiers in nutrition · 2025Article
- Energy efficiency identification and surface roughness prediction using cutting force signal for computer numerical controlled machine systems.Scientific reports · 2024Article
- EPAT: a user-friendly MATLAB toolbox for EEG/ERP data processing and analysis.Frontiers in neuroinformatics · 2024Article
- Maximizing the potential of EEG as a developmental neuroscience tool.Developmental cognitive neuroscience · 2023Article
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4 authors.
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Abstract
This EEG methods tutorial provides both a conceptual and practical introduction to a promising data reduction approach for time-frequency representations of EEG data: Time-Frequency Principal Components Analysis (TF-PCA). Briefly, the unique value of TF-PCA is that it provides a data-reduction approach that does not rely on strong a priori constraints regarding the specific timing or frequency boundaries for an effect of interest. Given that the time-frequency characteristics of various neurocognitive process are known to change across development, the TF-PCA approach is thus particularly well suited for the analysis of developmental TF data. This tutorial provides the background knowledge, theory, and practical information needed to allow individuals with basic EEG experience to begin applying the TF-PCA approach to their own data. Crucially, this tutorial article is accompanied by a companion GitHub repository that contains example code, data, and a step-by-step guide of how to perform TF-PCA: https://github.com/NDCLab/tfpca-tutorial. Although this tutorial is framed in terms of the utility of TF-PCA for developmental data, the theory, protocols and code covered in this tutorial article and companion GitHub repository can be applied more broadly across populations of interest.
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