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. 2024 Oct 12;11(10):1018.
doi: 10.3390/bioengineering11101018.

Optimizing EEG Signal Integrity: A Comprehensive Guide to Ocular Artifact Correction

Vincenzo Ronca  1   2 , Rossella Capotorto  3 , Gianluca Di Flumeri  2   4 , Andrea Giorgi  2   3 , Alessia Vozzi  2 , Daniele Germano  1 , Valerio Di Virgilio  1 , Gianluca Borghini  2   4 , Giulia Cartocci  2   4 , Dario Rossi  4 , Bianca M S Inguscio  1   2 , Fabio Babiloni  2   5   6 , Pietro Aricò  1   2
Affiliations

Optimizing EEG Signal Integrity: A Comprehensive Guide to Ocular Artifact Correction

Vincenzo Ronca et al. Bioengineering (Basel). .

Abstract

Ocular artifacts, including blinks and saccades, pose significant challenges in the analysis of electroencephalographic (EEG) data, often obscuring crucial neural signals. This tutorial provides a comprehensive guide to the most effective methods for correcting these artifacts, with a focus on algorithms designed for both laboratory and real-world settings. We review traditional approaches, such as regression-based techniques and Independent Component Analysis (ICA), alongside more advanced methods like Artifact Subspace Reconstruction (ASR) and deep learning-based algorithms. Through detailed step-by-step instructions and comparative analysis, this tutorial equips researchers with the tools necessary to maintain the integrity of EEG data, ensuring accurate and reliable results in neurophysiological studies. The strategies discussed are particularly relevant for wearable EEG systems and real-time applications, reflecting the growing demand for robust and adaptable solutions in applied neuroscience.

Keywords: EEG; ocular artifacts; signal processing.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Raw EEG signal on frontal electrodes showing ocular artifacts, which can be easily identified due to their larger amplitudes compared to the EEG signal.
Figure 2
Figure 2
Signal composition block diagram.
Figure 3
Figure 3
Raw EEG signal affected by ocular artifacts. Such artifacts can be easily visually recognized as the prominent peaks visible along the signal trace.
Figure 4
Figure 4
Example of artifactual component derived from the EEG signal affected by ocular artifacts through the regression-based algorithm.
Figure 5
Figure 5
Overlapped representation of the raw (orange line) and clean (blue line) EEG signals. The figure shows how the algorithm successfully identified and corrected the ocular artifacts.
Figure 6
Figure 6
Block diagram of the principal steps for approaching the identification and correction of ocular artifacts from an EEG signal through a regression-based method.
Figure 7
Figure 7
Example of ICA’s performance in removing ocular artifacts. The presented plots show: (i) the raw EEG from frontal electrodes; (ii) the first five components from ICA, ordered by energy; and (iii) the clean EEG from the same electrodes after removing the artifactual components (specifically, the first and second components). Green rectangles highlight blink patterns in both the raw EEG and the ICA components, while red rectangles indicate saccade patterns. After cleaning the EEG signal, these rectangles no longer contain artifact patterns, demonstrating the effectiveness of the artifact removal process.
Figure 8
Figure 8
Representation of the ASR method performance for correcting ocular blink artifacts from the EEG signal. The figure shows how the method was effective in identifying and correcting the ocular artifacts from the raw EEG signal (green line) and obtaining the clean (red line) EEG trace.
See this image and copyright information in PMC

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