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

Overview

Journal Bioengineering (Basel)

Date 2024 Oct 25

PMID 39451394

Authors

Vincenzo Ronca

Rossella Capotorto

Gianluca Di Flumeri

Andrea Giorgi

Alessia Vozzi

Daniele Germano

Valerio Di Virgilio

Gianluca Borghini

Giulia Cartocci

Dario Rossi

Bianca M S Inguscio

Fabio Babiloni

Pietro Arico

Affiliations

Soon will be listed here.

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.

Citing Articles

The Effect of EEG Biofeedback Training Frequency and Environmental Conditions on Simple and Complex Reaction Times.

Dariusz S, Kostrzewa M, Magdalena P, Markowski J, Pilch J, Zak M Bioengineering (Basel). 2025; 11(12.

PMID: 39768026 PMC: 11673860. DOI: 10.3390/bioengineering11121208.

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