Hearables: Artefact removal in Ear-EEG for continuous 24/7 monitoring

Abstract

Ear-worn devices offer the opportunity to measure vital signals in a 24/7 fashion, without the need of a clinician. These devices are however prone to motion artefacts, so that entire epochs of artefact-corrupt recordings are routinely discarded. This work aims at reducing the impact of artefacts introduced by a series of common real life daily activities such as talking, chewing, and walking while recording Electroencephalogram (EEG) from the ear canal. The approach used employs multiple external sensors, such as microphones and an accelerometer as means to capture the artefact. The proposed algorithm is a combination of Noise-Assisted Multivariate Empirical Mode Decomposition (NA-MEMD) with Adaptive Noise Cancellation (ANC), where each pair (EEG and motion sensors) of Intrinsic Mode Functions (IMFs) within NA-MEMD is fed independently to multiple Normalised Least Mean Square (NLMS) adaptive filters. The resulting denoised IMFs are then added up again to reconstruct the denoised EEG signal. Results across multiple subjects show that the so denoised EEG signals have reduced power in the frequency range occupied by artefacts. Also, different sensors provide different denoising performance in the tested artefacts, with the microphones being more sensitive to artefacts which cause internal motion within the ear-canal, such as chewing, and the accelerometer being more suitable for artefacts which come from full body movements of the subjects, such as walking.