Robust artifactual independent component classification for BCI practitioners

By Irene Winkler, Stephanie Brandl, Franziska Horn, Eric Waldburger, C. Allefeld, Michael W. Tangermann. Journal of Neural Engineering, 2014

313 citations recorded by the source corpus.

Objective. EEG artifacts of non-neural origin can be separated from neural signals by independent component analysis (ICA). It is unclear (1) how robustly recently proposed artifact classifiers transfer to novel users, novel paradigms or changed electrode setups, and (2) how artifact cleaning by a machine learning classifier impacts the performance of brain–computer interfaces (BCIs). Approach. Addressing (1), the robustness of different strategies with respect to the transfer between paradigms and electrode setups of a recently proposed classifier is investigated on offline data from 35 users and 3 EEG paradigms, which contain 6303 expert-labeled components from two ICA and preprocessing variants. Addressing (2), the effect of artifact removal on single-trial BCI classification is estimated on BCI trials from 101 users and 3 paradigms. Main results. We show that (1) the proposed artifact classifier generalizes to completely different EEG paradigms. To obtain similar results under massively reduced electrode setups, a proposed novel strategy improves artifact classification. Addressing (2), ICA artifact cleaning has little influence on average BCI performance when analyzed by state-of-the-art BCI methods. When slow motor-related features are exploited, performance varies strongly between individuals, as artifacts may obstruct relevant neural activity or are inadvertently used for BCI control. Significance. Robustness of the proposed strategies can be reproduced by EEG practitioners as the method is made available as an EEGLAB plug-in.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/f88da04b87daada14ee9b63ced185d54367aefaa

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In Cognitive Neuroscience

Citations in the corpus

Removal of Artifacts from EEG Signals: A Review (2019, methodology): “[99] removed contaminated EMG signals from EEG data.”
On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP (2015, methodology): “For varying cutoff frequencies, we train ICA on highpass filtered data, automatically classify the resulting ICs with MARA [7], [8], and apply the obtained filter weights on the original non high-pass filtered data (see Fig.”
The Harvard Automated Processing Pipeline for Electroencephalography (HAPPE): Standardized Processing Software for Developmental and High-Artifact Data (2018, methodology): “Automated component rejection is achieved through the Multiple Artifact Rejection Algorithm (MARA), a machine-learning algorithm that evaluates the ICA-derived components (Winkler et al., 2011, 2014).”
Alterations in aperiodic and periodic EEG activity in young children with Down syndrome (2024, unknown): “…to 250 Hz and then run through the HAPPE module which included 60 Hz line noise removal, bad channel rejection, and artifact removal using combined wavelet-enhanced independent component analysis (ICA) and Multiple Artifact Rejection Algorithm (MARA (Winkler et al., 2011; Winkler et al., 2014)).”
Alterations in aperiodic and periodic EEG activity in young children with Down syndrome (2024, unknown): “…to 250 Hz and then run through the HAPPE module which included 60 Hz line noise removal, bad channel rejection, and artifact removal using combined wavelet-enhanced independent component analysis (ICA) and Multiple Artifact Rejection Algorithm (MARA (Winkler et al., 2011; Winkler et al., 2014)).”
Theta power reduction and theta–gamma coupling desynchronization are associated with working memory interference and anxiety symptoms in panic disorder: a retrospective study (2024, unknown): “Components with artifact probabilities > 0.8 were eliminated using ICA, an established method for effectively isolating ocular, electromyographic, and electrocardiographic artifacts [62].”
The Functional Interactions between Cortical Regions through Theta-Gamma Coupling during Resting-State and a Visual Working Memory Task (2022, methodology): “The Harvard Automated Processing Pipeline for EEG [87] was used to automate preprocessing and artifact correction using Wavelet-enhanced independent component analysis (ICA) and the Multiple Artifact Rejection Algorithm (MARA) [88].”
How the influence of cingulate-lingual interactions on event segmentation changes from early to late adolescence (2026, unknown): “EOG artefacts were removed using a subtraction method 50 , and remaining artefacts (e.g., muscle, heart, and residual eye artefacts) were identified and eliminated through independent component analysis (ICA) using the multiple artifact rejection algorithm (MARA 51,52 ).”