Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering

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 local movement data. This 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, we find that the performance varies, necessitating the need for our 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.