Bi-Smoothed Functional Independent Component Analysis for EEG Artifact Removal

TL;DR

This paper introduces a novel functional independent component analysis method using spectral kurtosis decomposition and smoothing techniques to effectively remove artifacts from EEG signals, improving neural signal extraction.

Contribution

It proposes a new spectral kurtosis-based ICA approach with smoothing and cross-validation for EEG artifact removal, enhancing signal quality.

Findings

Effective removal of EEG artifacts demonstrated on real data

Controlled high-frequency neural remnants for cleaner signals

Available R package facilitates practical application

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 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.