Open AccessMotion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering
Author(s) -
Beach Christopher,
Li Mingjie,
Balaban Ertan,
Casson Alexander J.
Publication year - 2021
Publication title -
healthcare technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 19
ISSN - 2053-3713
DOI - 10.1049/htl2.12016
Subject(s) - computer science , electroencephalography , signal (programming language) , artificial intelligence , computer vision , inertial measurement unit , signal processing , motion (physics) , adaptive filter , pattern recognition (psychology) , computer hardware , digital signal processing , algorithm , psychology , psychiatry , programming language
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.