The importance of ocular artifact removal in single-trial ERP analysis: The case of the N250 in face learning. (January 2023)

• Record Type: Journal Article
• Authors: Kotowski, Krzysztof; Ochab, Jeremi; Stapor, Katarzyna; Sommer, Werner
• Is Part Of: Biomedical signal processing and control. Volume 79(2023)Part 1
• Publication Date: 2023-01
• Subjects: Event-related potentials -- Single-trial -- Ocular artifacts -- ICA -- N250 -- Face learning; EOG electrooculogram -- ERP event-related potential -- ICA independent components analysis -- IQR interquartile range -- OSF Open Science Framework -- RSS residual sum of squares -- SD standard deviation; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104115
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: The first published analysis of time series of single-trial N250 ERP. Highly reproducible pipeline for precise single-trial ERP analysis. New semi-automatic ocular artifact filtration method using ICA and EOG. Ocular artifact removal decreased RMSE by half in the simulation study. Ocular artifact removal improved the regression model fit by 25% ± 17% Abstract: Objective: Single-trial event-related potentials (ERPs) offer fine-grained information about the trajectories of the neurocognitive processes but are highly sensitive to any artifacts in the EEG signal. The primary aim of this study was to assess the impact of ocular artifact removal on the single-trial N250 ERP analysis of face learning in individual participants. Methods: We present a detailed description of our research-grade EEG hardware setup and a highly reproducible code (https://osf.io/aqhmn/ ) for generating time series of single-trial N250 ERP amplitudes and precise identification of a changepoint between face memory trace acquisition and maintenance. Ocular artifacts were removed using a new semi-automatic approach with only one hyperparameter based on the correlation between EEG components from independent component analysis (ICA) and the EOG signal. Results: Results from the simulation study showed that our ocular artifact filtration decreased the average RMSE by half and achieved the highest increase of SNR among all the compared methods. It decreased standard deviations and improved the fit of the broken-line regression models for all participants by 25% ± 17% (min. 2%, max. 63%). Conclusions and significance: Ocular artifact filtration had a substantial positive impact on the regression modeling of single-trial ERP amplitudes. Lack of ocular artifact removal can drastically distort the conclusions about the face learning process from single-trial N250 ERP experiments for individual participants. The changepoint locations changed for 13 out of 15 participants. This is the first published analysis of time series of single-trial N250 ERP amplitudes in face learning.