ArticleBiometrics2017
A multi-dimensional functional principal components analysis of EEG data.
Article in Biometrics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed, 64 citations in OpenAlex.
- Leveraging AI-Driven Neuroimaging Biomarkers for Early Detection and Social Function Prediction in Autism Spectrum Disorders: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- Central Posterior Envelopes for Bayesian Longitudinal Functional Principal Component Analysis.Statistics in biosciences · 2025Article
- DYNAMIC PREDICTION WITH MULTIVARIATE LONGITUDINAL OUTCOMES AND LONGITUDINAL MAGNETIC RESONANCE IMAGING DATA.The annals of applied statistics · 2025Article
- Functional analysis within latent states: A novel framework for analysing functional time series data.PloS one · 2025Article
- Lower-limb surface electromyographic differences in roundhouse kicks between elite and sub-elite taekwondo athletes: a functional principal component analysis.Frontiers in physiology · 2025Article
- Modeling intra-individual inter-trial EEG response variability in autism.Statistics in medicine · 2024Article
- Guaranteed Functional Tensor Singular Value Decomposition.Journal of the American Statistical Association · 2024Article
- Latent factor model for multivariate functional data.Biometrics · 2023Article
- Central Posterior Envelopes for Bayesian Functional Principal Component Analysis.Journal of data science : JDS · 2023Article
- Interpretable principal component analysis for multilevel multivariate functional data.Biostatistics (Oxford, England) · 2023Article
- Application of EEG in migraine.Frontiers in human neuroscience · 2023Review
- Article
- Bayesian analysis of longitudinal and multidimensional functional data.Biostatistics (Oxford, England) · 2022Article
- Covariate-adjusted hybrid principal components analysis for region-referenced functional EEG data.Statistics and its interface · 2022Article
- Neural Decoding of EEG Signals with Machine Learning: A Systematic Review.Brain sciences · 2021Review
- Multilevel modeling of spatially nested functional data: Spatiotemporal patterns of hospitalization rates in the US dialysis population.Statistics in medicine · 2021Article
- Principle ERP reduction and analysis: Estimating and using principle ERP waveforms underlying ERPs across tasks, subjects and electrodes.NeuroImage · 2020Article
- Hybrid principal components analysis for region-referenced longitudinal functional EEG data.Biostatistics (Oxford, England) · 2020Article
- Bayesian Semiparametric Functional Mixed Models for Serially Correlated Functional Data, with Application to Glaucoma Data.Journal of the American Statistical Association · 2019Article
- Inferring Brain Signals Synchronicity from a Sample of EEG Readings.Journal of the American Statistical Association · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
The electroencephalography (EEG) data created in event-related potential (ERP) experiments have a complex high-dimensional structure. Each stimulus presentation, or trial, generates an ERP waveform which is an instance of functional data. The experiments are made up of sequences of multiple trials, resulting in longitudinal functional data and moreover, responses are recorded at multiple electrodes on the scalp, adding an electrode dimension. Traditional EEG analyses involve multiple simplifications of this structure to increase the signal-to-noise ratio, effectively collapsing the functional and longitudinal components by identifying key features of the ERPs and averaging them across trials. Motivated by an implicit learning paradigm used in autism research in which the functional, longitudinal, and electrode components all have critical interpretations, we propose a multidimensional functional principal components analysis (MD-FPCA) technique which does not collapse any of the dimensions of the ERP data. The proposed decomposition is based on separation of the total variation into subject and subunit level variation which are further decomposed in a two-stage functional principal components analysis. The proposed methodology is shown to be useful for modeling longitudinal trends in the ERP functions, leading to novel insights into the learning patterns of children with Autism Spectrum Disorder (ASD) and their typically developing peers as well as comparisons between the two groups. Finite sample properties of MD-FPCA are further studied via extensive simulations.
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Registered trials
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.