ArticleHealthcare technology letters2021
Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering.
Article in Healthcare technology letters, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- On-Skin Wearable Health Monitoring Devices: Recent Trends and Perspectives.Sensors (Basel, Switzerland) · 2026Review
- Improving Signal Quality in Non-Contact Electrocardiography: Novel Strategy for Motion Artifact Reduction.Sensors (Basel, Switzerland) · 2026Article
- Dual-sided EEG electrode signals have varying correlations that depend on movement characteristics.Scientific reports · 2025Article
- Advancements and Applications of EEG in Gustatory Perception.Brain sciences · 2025Review
- The Feasibility and Test-Retest Reliability of Wireless Dry-Electrode EEG During a Dynamic Psychomotor Virtual Reality Task.Brain and behavior · 2025Article
- Motion artifacts in capacitive ECG monitoring systems: a review of existing models and reduction techniques.Medical & biological engineering & computing · 2024Review
- iCanClean Removes Motion, Muscle, Eye, and Line-Noise Artifacts from Phantom EEG.Sensors (Basel, Switzerland) · 2023Article
- Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review.Frontiers in human neuroscience · 2023Review
- Towards On-Device Dehydration Monitoring Using Machine Learning from Wearable Device's Data.Sensors (Basel, Switzerland) · 2022Article
- Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering.Healthcare technology letters · 2021Article
- A Privacy-Preserving Markov Chain-Based Framework for Robust Motor Imagery EEG Classification in Brain-Computer Interfaces.Healthcare technology lettersArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper presents a new active electrode design for electroencephalogram (EEG) and electrocardiogram (ECG) sensors based on inertial measurement units to remove motion artefacts during signal acquisition. Rather than measuring motion data from a single source for the entire recording unit, inertial measurement units are attached to each individual EEG or ECG electrode to collect local movement data. This data is then used to remove the motion artefact by using normalised least mean square adaptive filtering. Results show that the proposed active electrode design can reduce motion contamination from EEG and ECG signals in chest movement and head swinging motion scenarios. However, it is found that the performance varies, necessitating the need for the algorithm to be paired with more sophisticated signal processing to identify scenarios where it is beneficial in terms of improving signal quality. The new instrumentation hardware allows data driven artefact removal to be performed, providing a new data driven approach compared to widely used blind-source separation methods, and helps enable in the wild EEG recordings to be performed.
Identifiers
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Registered trials
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