| Autor: |
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 Aricò |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Bioengineering, Vol 11, Iss 10, p 1018 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2306-5354 |
| DOI: |
10.3390/bioengineering11101018 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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