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Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering
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  • Christopher Beach ,
  • Mingjie Li ,
  • Ertan Balaban ,
  • Alex Casson
Christopher Beach
The University of Manchester, The University of Manchester

Corresponding Author:[email protected]

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Mingjie Li
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Ertan Balaban
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Alex Casson
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This paper presents a new active electrode design for electroencephalogram (EEG) and electrocardiogram (ECG) sensors based on Inertial Measurement Units (IMUs) to remove motion artefacts during signal acquisition. Rather than measuring motion data from a single source for the entire recording unit, IMUs are attached to each individual EEG or ECG electrode to collect more local movement data. This movement data is then used to remove the motion artefact by using Normalised Least Mean Square (NLMS) 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 the performance depends on the quality of the input signal with the algorithm providing better performance on signals with lower signal-to-noise ratios. The new instrumentation hardware allows data driven artefact removal to be performed, providing a new approach compared to widely used, non-parametric, blind-source separation methods, and helps enable \emph{in the wild} EEG recordings to be performed.
Oct 2021Published in Healthcare Technology Letters volume 8 issue 5 on pages 128-138. 10.1049/htl2.12016