ReviewFrontiers in human neuroscience2023
Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review.
Review in Frontiers in human neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 16 citations in OpenAlex.
- Isolating error-based and reward-based learning in a real-world task via gradual perturbations in embodied VR.iScience · 2026Article
- Full-Stack Architectures for Intelligent Brain-Computer Interfaces.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Advances in the Application of Brain-Computer Interface-Based Neurofeedback Training in the Rehabilitation of Patients with Major Depressive Disorder.Neuropsychiatric disease and treatment · 2026Review
- Dual-sided EEG electrode signals have varying correlations that depend on movement characteristics.Scientific reports · 2025Article
- EEG-cleanse: an automated pipeline for cleaning electroencephalography recordings during full-body movement.MethodsX · 2025Article
- [Prospects and technical challenges of non-invasive brain-computer interfaces in manned space missions].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2025Article
- Instantiating the onEEGwaveLAD Framework for Real-Time Muscle Artefact Identification and Mitigation in EEG Signals.Sensors (Basel, Switzerland) · 2025Article
- Investigation of mothers' elicited infant-directed speech and singing for preterm infants.Pediatric research · 2025Article
- Electrophysiological Insights in Exergaming-Electroencephalography Data Recording and Movement Artifact Detection: Systematic Review.JMIR serious games · 2025Review
- Brain-computer Interaction in the Smart Era.Current medical science · 2024Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.