ArticleMedical & biological engineering & computing2020
The influence of motor tasks and cut-off parameter selection on artifact subspace reconstruction in EEG recordings.
Article in Medical & biological engineering & computing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- A Systematic Review of Techniques for Artifact Detection and Artifact Category Identification in Electroencephalography from Wearable Devices.Sensors (Basel, Switzerland) · 2025Pooled it
- Editorial: Passive brain-computer interfaces: moving from lab to real-world application.Frontiers in computational neuroscience · 2026Article
- Dual-sided EEG electrode signals have varying correlations that depend on movement characteristics.Scientific reports · 2025Article
- A Comparison of Approaches for Motion Artifact Removal from Wireless Mobile EEG During Overground Running.Sensors (Basel, Switzerland) · 2025Article
- Advancing Alzheimer's disease detection: a novel convolutional neural network based framework leveraging EEG data and segment length analysis.Brain informatics · 2025Article
- Dynamical Embedding of Single-Channel Electroencephalogram for Artifact Subspace Reconstruction.Sensors (Basel, Switzerland) · 2024Article
- Optimizing EEG Signal Integrity: A Comprehensive Guide to Ocular Artifact Correction.Bioengineering (Basel, Switzerland) · 2024Article
- Shared EEG correlates between non-REM parasomnia experiences and dreams.Nature communications · 2024Article
- One out of ten independent components shows flipped polarity with poorer data quality: EEG database study.Human brain mapping · 2024Article
- Artifact subspace reconstruction: a candidate for a dream solution for EEG studies, sleep or awake.Sleep · 2023Article
- Neural oscillatory activity and connectivity in children who stutter during a non-speech motor task.Journal of neurodevelopmental disorders · 2023Article
- A Dynamical Systems Approach to Characterizing Brain-Body Interactions during Movement: Challenges, Interpretations, and Recommendations.Sensors (Basel, Switzerland) · 2023Review
- Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review.Frontiers in human neuroscience · 2023Review
- Independent evaluation of the harvard automated processing pipeline for Electroencephalography 1.0 using multi-site EEG data from children with Fragile X Syndrome.Journal of neuroscience methods · 2022Article
- Galvanic Vestibular Stimulation Effects on EEG Biomarkers of Motor Vigor in Parkinson's Disease.Frontiers in neurology · 2021Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
Advances in EEG filtering algorithms enable analysis of EEG recorded during motor tasks. Although methods such as artifact subspace reconstruction (ASR) can remove transient artifacts automatically, there is virtually no knowledge about how the vigor of bodily movements affects ASRs performance and optimal cut-off parameter selection process. We compared the ratios of removed and reconstructed EEG recorded during a cognitive task, single-leg stance, and fast walking using ASR with 10 cut-off parameters versus visual inspection. Furthermore, we used the repeatability and dipolarity of independent components to assess their quality and an automatic classification tool to assess the number of brain-related independent components. The cut-off parameter equivalent to the ratio of EEG removed in manual cleaning was strictest for the walking task. The quality index of independent components, calculated using RELICA, reached a maximum plateau for cut-off parameters of 10 and higher across all tasks while dipolarity was largely unaffected. The number of independent components within each task remained constant, regardless of the cut-off parameter used. Surprisingly, ASR performed better in motor tasks compared with non-movement tasks. The quality index seemed to be more sensitive to changes induced by ASR compared to dipolarity. There was no benefit of using cut-off parameters less than 10. Graphical abstract The graphical abstract shows the three tasks performed during EEG recording, the two processing pipelines (manual and artifact subspace reconstruction), and the metrics the conclusion is based on.
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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.