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...
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Autor*in:	Mathias Schmoigl-Tonis [verfasserIn] Christoph Schranz [verfasserIn] Gernot R. Müller-Putz [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	brain-computer interface (BCI) electroencephalography (EEG) artifact removal motion artifact muscle artifact fasciculation

Übergeordnetes Werk:	In: Frontiers in Human Neuroscience - Frontiers Media S.A., 2008, 17(2023)
Übergeordnetes Werk:	volume:17 ; year:2023

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fnhum.2023.1251690

Katalog-ID:	DOAJ092703836

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source	In Frontiers in Human Neuroscience 17(2023) volume:17 year:2023
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callnumber-first	R - Medicine
author	Mathias Schmoigl-Tonis
spellingShingle	Mathias Schmoigl-Tonis misc RC321-571 misc brain-computer interface (BCI) misc electroencephalography (EEG) misc artifact removal misc motion artifact misc muscle artifact misc fasciculation misc Neurosciences. Biological psychiatry. Neuropsychiatry Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review
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topic_title	RC321-571 Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review brain-computer interface (BCI) electroencephalography (EEG) artifact removal motion artifact muscle artifact fasciculation
topic	misc RC321-571 misc brain-computer interface (BCI) misc electroencephalography (EEG) misc artifact removal misc motion artifact misc muscle artifact misc fasciculation misc Neurosciences. Biological psychiatry. Neuropsychiatry
topic_unstemmed	misc RC321-571 misc brain-computer interface (BCI) misc electroencephalography (EEG) misc artifact removal misc motion artifact misc muscle artifact misc fasciculation misc Neurosciences. Biological psychiatry. Neuropsychiatry
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title	Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review
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title_full	Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review
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title_sort	methods for motion artifact reduction in online brain-computer interface experiments: a systematic review
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title_auth	Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review
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