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dc.contributor.authorBeach, Christopher; orcid: 0000-0003-4964-3173; email: christopher.beach@manchester.ac.uk
dc.contributor.authorLi, Mingjie
dc.contributor.authorBalaban, Ertan; orcid: 0000-0001-7904-6172
dc.contributor.authorCasson, Alexander J.; orcid: 0000-0003-1408-1190
dc.date.accessioned2021-06-25T08:18:51Z
dc.date.available2021-06-25T08:18:51Z
dc.date.issued2021-06-24
dc.date.submitted2021-01-22
dc.identifierhttps://chesterrep.openrepository.com/bitstream/handle/10034/625039/htl2.12016.xml?sequence=2
dc.identifierhttps://chesterrep.openrepository.com/bitstream/handle/10034/625039/htl2.12016.pdf?sequence=3
dc.identifier.citationHealthcare Technology Letters
dc.identifier.urihttp://hdl.handle.net/10034/625039
dc.descriptionFrom Wiley via Jisc Publications Router
dc.descriptionHistory: received 2021-01-22, rev-recd 2021-05-14, accepted 2021-05-28, pub-electronic 2021-06-24
dc.descriptionArticle version: VoR
dc.descriptionPublication status: Published
dc.descriptionFunder: Engineering and Physical Sciences Research Council; Id: http://dx.doi.org/10.13039/501100000266; Grant(s): EP/S020179/1, EP/P02713X/1
dc.description.abstractAbstract: 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.
dc.languageen
dc.rightsLicence for VoR version of this article: http://creativecommons.org/licenses/by/4.0/
dc.sourceissn: 2053-3713
dc.subjectORIGINAL RESEARCH PAPER
dc.subjectORIGINAL RESEARCH PAPERS
dc.titleMotion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering
dc.typearticle
dc.date.updated2021-06-25T08:18:50Z
dc.date.accepted2021-05-28


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