On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP

Abstract

Standard artifact removal methods for electroencephalographic (EEG) signals are either based on Independent Component Analysis (ICA) or they regress out ocular activity measured at electrooculogram (EOG) channels. Successful ICA-based artifact reduction relies on suitable pre-processing. Here we systematically evaluate the effects of high-pass filtering at different frequencies. Offline analyses were based on event-related potential data from 21 participants performing a standard auditory oddball task and an automatic artifactual component classifier method (MARA). As a pre-processing step for ICA, high-pass filtering between 1-2 Hz consistently produced good results in terms of signal-to-noise ratio (SNR), single-trial classification accuracy and the percentage of `near-dipolar' ICA components. Relative to no artifact reduction, ICA-based artifact removal significantly improved SNR and classification accuracy. This was not the case for a regression-based approach to remove EOG artifacts.

ARTIST: A fully automated artifact rejection algorithm for single‐pulse TMS‐EEG data

“…Following decay artifact removal, continuous EEG recordings are high‐pass filtered (1 Hz cutoff, zero‐phase FIR filter), which facilitates ICA estimation (a) by increasing the mutual independence between sources, as low frequency trends are likely dependent, and (b) by enhancing the dipolarity of the ICs (Winkler et al, ). In addition, a 100‐Hz zero‐phase FIR low‐pass filter is employed to attenuate high‐frequency noise, and a 60 Hz zero‐phase FIR notch filter removes 60 Hz AC line noise.…”

“…The second stage filters the continuous data to remove the AC line noise and high-frequency noise, and then rejects bad epochs and channels from the epoched data. The third stage removes the remaining artifacts (residual decay artifacts, ocular artifacts, EKG artifact, and persistent EMG artifact) from the epoched data, after which the data are rereferenced to the common average and baseline corrected dipolarity of the ICs (Winkler et al, 2015). In addition, a 100-Hz zerophase FIR low-pass filter is employed to attenuate high-frequency noise, and a 60 Hz zero-phase FIR notch filter removes 60 Hz AC line noise.…”

Cortical oscillations and entrainment in speech processing during working memory load

“…The logistic infomax independent component analysis (ICA) algorithm (Bell & Sejnowski, 1995;Delorme & Makeig, 2004;Winkler et al, 2015) was used to decompose the re-referenced EEG data from each subject high-pass filtered at 1 Hz. The components were visually inspected, and artefactual components were rejected.…”

“…The remaining components were identified as either muscle or cardiac-related artefacts that appeared consistently across trials. The ICA-derived mixing matrices were thereafter used to spatially filter out artefactual activity from the original EEG data high-pass filtered at 0.5 Hz (Winkler et al, 2015). Trials were inspected visually for artefacts after ICA cleaning, and remaining bad trials were removed.…”

Variability of ICA decomposition may impact EEG signals when used to remove eyeblink artifacts

“…As low-frequency aspects of the EEG signal typically account for large proportions of variance, the application of a high-pass filter can improve the ICA decomposition by removing slow drifts and DC components (Winkler, Debener, Muller, & Tangermann, 2015; Zakeri, Assecondi, Bagshaw, & Arvanitis, 2014). Similarly, including electrode channels located near the eye can improve the ICA decomposition for the purposes of artifact removal, as the electrodes provide greater information for the ICA algorithm to separate the artifact from the background EEG (Zakeri et al, 2014).…”