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Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review
Brain-computer interfaces (BCIs) have emerged as a promising technology for enhancing communication between the human brain and external devices. Electroencephalography (EEG) is particularly promising in this regard because it has high temporal resolution and can be easily worn on the head in everyd...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10619676/ https://www.ncbi.nlm.nih.gov/pubmed/37920561 http://dx.doi.org/10.3389/fnhum.2023.1251690 |
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author | Schmoigl-Tonis, Mathias Schranz, Christoph Müller-Putz, Gernot R. |
author_facet | Schmoigl-Tonis, Mathias Schranz, Christoph Müller-Putz, Gernot R. |
author_sort | Schmoigl-Tonis, Mathias |
collection | PubMed |
description | Brain-computer interfaces (BCIs) have emerged as a promising technology for enhancing communication between the human brain and external devices. Electroencephalography (EEG) is particularly promising in this regard because it has high temporal resolution and can be easily worn on the head in everyday life. However, motion artifacts caused by muscle activity, fasciculation, cable swings, or magnetic induction pose significant challenges in real-world BCI applications. In this paper, we present a systematic review of methods for motion artifact reduction in online BCI experiments. Using the PRISMA filter method, we conducted a comprehensive literature search on PubMed, focusing on open access publications from 1966 to 2022. We evaluated 2,333 publications based on predefined filtering rules to identify existing methods and pipelines for motion artifact reduction in EEG data. We present a lookup table of all papers that passed the defined filters, all used methods, and pipelines and compare their overall performance and suitability for online BCI experiments. We summarize suitable methods, algorithms, and concepts for motion artifact reduction in online BCI applications, highlight potential research gaps, and discuss existing community consensus. This review aims to provide a comprehensive overview of the current state of the field and guide researchers in selecting appropriate methods for motion artifact reduction in online BCI experiments. |
format | Online Article Text |
id | pubmed-10619676 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-106196762023-11-02 Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review Schmoigl-Tonis, Mathias Schranz, Christoph Müller-Putz, Gernot R. Front Hum Neurosci Human Neuroscience Brain-computer interfaces (BCIs) have emerged as a promising technology for enhancing communication between the human brain and external devices. Electroencephalography (EEG) is particularly promising in this regard because it has high temporal resolution and can be easily worn on the head in everyday life. However, motion artifacts caused by muscle activity, fasciculation, cable swings, or magnetic induction pose significant challenges in real-world BCI applications. In this paper, we present a systematic review of methods for motion artifact reduction in online BCI experiments. Using the PRISMA filter method, we conducted a comprehensive literature search on PubMed, focusing on open access publications from 1966 to 2022. We evaluated 2,333 publications based on predefined filtering rules to identify existing methods and pipelines for motion artifact reduction in EEG data. We present a lookup table of all papers that passed the defined filters, all used methods, and pipelines and compare their overall performance and suitability for online BCI experiments. We summarize suitable methods, algorithms, and concepts for motion artifact reduction in online BCI applications, highlight potential research gaps, and discuss existing community consensus. This review aims to provide a comprehensive overview of the current state of the field and guide researchers in selecting appropriate methods for motion artifact reduction in online BCI experiments. Frontiers Media S.A. 2023-10-18 /pmc/articles/PMC10619676/ /pubmed/37920561 http://dx.doi.org/10.3389/fnhum.2023.1251690 Text en Copyright © 2023 Schmoigl-Tonis, Schranz and Müller-Putz. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Human Neuroscience Schmoigl-Tonis, Mathias Schranz, Christoph Müller-Putz, Gernot R. Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review |
title | Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review |
title_full | Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review |
title_fullStr | Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review |
title_full_unstemmed | Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review |
title_short | Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review |
title_sort | methods for motion artifact reduction in online brain-computer interface experiments: a systematic review |
topic | Human Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10619676/ https://www.ncbi.nlm.nih.gov/pubmed/37920561 http://dx.doi.org/10.3389/fnhum.2023.1251690 |
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