Artifact Removal in tACS-EEG Recordings: A Combined Methodology Based on the Empirical Wavelet Transform.

Abstract

Transcranial alternating current stimulation (tACS) is a non-invasive brain stimulation technique that modulates brain activity, which yields promise for achieving desired behavioral outcomes in different contexts. Combining tACS with electroencephalography (EEG) allows for the monitoring of the real-time effects of stimulation. However, the EEG signal recorded with simultaneous tACS is largely contaminated by stimulation-induced artifacts. In this work, we examine the combination of the empirical wavelet transform (EWT) with three blind source separation (BSS) methods: principal component analysis (PCA), multiset canonical correlation analysis (MCCA) and independent vector analysis (IVA), aiming to remove artifacts in tACS-contaminated EEG recordings. Using simulated data, we show that EWT followed by IVA achieves the best performance. Using experimental data, we show that BSS combined with EWT performs better compared to standard BSS methodology in terms of preserving useful information while eliminating artifacts.