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

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.

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Publication Details

Journal
Bioengineering
Published
2024-10-12
DOI
https://doi.org/10.3390/bioengineering11101018
Citations
10
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
3.43

Funders

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article

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

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Bioengineering
EEG and Brain-Computer Interfaces
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article

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

Fabio Babiloni, Andrea Giorgi, Giulia Cartocci, Alessia Vozzi, Gianluca Di Flumeri, Daniele Germano, Bianca Maria Serena Inguscio, Pietro Aricò, Valerio Di Virgilio, D. Rossi, Gianluca Borghini, Vincenzo Ronca, Rossella Capotorto
article en
10 citations

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.

BioengineeringVol. 11(10)
Hangzhou Dianzi University (CN), Sapienza University of Rome (IT)
Horizon 2020 Framework Programme
Quality Education
Openalex Percentile: Top 7%
EEG and Brain-Computer Interfaces
3.43
DOIOpen Access PDFOpenAlex
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