# HEAR to remove pops and drifts: the high-variance electrode artifact removal (HEAR) algorithm

## TL;DR

The HEAR algorithm effectively removes transient electrode pop and drift artifacts from EEG signals, improving data quality for neuroimaging and BCI applications by outperforming existing methods.

## Contribution

Introduction of the HEAR and oHEAR algorithms, novel open-source methods that outperform state-of-the-art artifact correction techniques in EEG signal processing.

## Key findings

- Attenuated PD artifacts by approximately 25 dB.
- Reduced outlier trials by half in real EEG data.
- Maintained waveform integrity during movement tasks.

## Abstract

A high fraction of artifact-free signals is highly desirable in functional neuroimaging and brain-computer interfacing (BCI). We present the high-variance electrode artifact removal (HEAR) algorithm to remove transient electrode pop and drift (PD) artifacts from electroencephalographic (EEG) signals. Transient PD artifacts reflect impedance variations at the electrode scalp interface that are caused by ion concentration changes. HEAR and its online version (oHEAR) are open-source and publicly available. Both outperformed state of the art offline and online transient, high-variance artifact correction algorithms for simulated EEG signals. (o)HEAR attenuated PD artifacts by approx. 25 dB, and at the same time maintained a high SNR during PD artifact-free periods. For real-world EEG data, (o)HEAR reduced the fraction of outlier trials by half and maintained the waveform of a movement related cortical potential during a center-out reaching task. In the case of BCI training, using oHEAR can improve the reliability of the feedback a user receives through reducing a potential negative impact of PD artifacts.