pfica: Independent Components Analysis Techniques for Functional Data

Vidal, Marc; Aguilera, Ana Mª

doi:10.32614/cran.package.pfica

CRAN: Contributed Packages 2020

DOI: 10.32614/cran.package.pfica

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pfica: Independent Components Analysis Techniques for Functional Data

Marc Vidal,

Ana Mª Aguilera

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2021

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Bi-Smoothed Functional Independent Component Analysis for EEG Artifact Removal

2021

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Motivated by mapping adverse artifactual events caused by body movements in electroencephalographic (EEG) signals, we present a functional independent component analysis based on the spectral decomposition of the kurtosis operator of a smoothed principal component expansion. A discrete roughness penalty is introduced in the orthonormality constraint of the covariance eigenfunctions in order to obtain the smoothed basis for the proposed independent component model. To select the tuning parameters, a cross-validation method that incorporates shrinkage is used to enhance the performance on functional representations with a large basis dimension. This method provides an estimation strategy to determine the penalty parameter and the optimal number of components. Our independent component approach is applied to real EEG data to estimate genuine brain potentials from a contaminated signal. As a result, it is possible to control high-frequency remnants of neural origin overlapping artifactual sources to optimize their removal from the signal. An R package implementing our methods is available at CRAN.

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Bi-Smoothed Functional Independent Component Analysis for EEG Artifact Removal

2021

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pfica: Independent Components Analysis Techniques for Functional Data

Cited by 1 publication

References 2 publications

Bi-Smoothed Functional Independent Component Analysis for EEG Artifact Removal

Bi-Smoothed Functional Independent Component Analysis for EEG Artifact Removal

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