Bi-Smoothed Functional Independent Component Analysis for EEG Artifact Removal

Marc Vidal, Mattia Rosso and Ana M. Aguilera
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Marc Vidal: Institute of Psychoacoustics and Electronic Music (IPEM), Ghent University, 9000 Ghent, Belgium
Mattia Rosso: Institute of Psychoacoustics and Electronic Music (IPEM), Ghent University, 9000 Ghent, Belgium
Ana M. Aguilera: Department of Statistics and O.R. and IMAG, University of Granada, 18071 Granada, Spain

Mathematics, 2021, vol. 9, issue 11, 1-17

Abstract: 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.

Keywords: functional data; functional kurtosis; penalized splines; smoothed principal components; auditory–motor coupling task; EEG; motion artifacts (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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