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

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 opensource 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.

Distinct cortical networks for hand movement initiation and directional processing: An EEG study

“…The EEG channels were subsequently converted to a common average reference (CAR). We applied the high-variance electrode artifact removal (HEAR) algorithm to attenuate occasional, single electrode pops and low-frequency drifts ( Kobler et al., 2019 ). HEAR monitors the variance of each EEG channel.…”

Mynd

“…This feedback will need to be robust to the lower signal-to-noise ratio and higher susceptibility to involuntary head-muscle artifacts around the ears and the forehead, where the sensors are located. As one promising approach to implement robust feedback, concurrent work in [29] explored an adaptive signal-filtering method that relies on signal-variation, similar to the fitting algorithm presented here. Providing immediately accessible daily decoding scores could be implemented on the device by utilizing prior information from higher quality laboratory data via transfer-learning, as shown in this offline analysis.…”

FiNN: A toolbox for neurophysiological network analysis

“…NeuroKit2 (Makowski, 2016) Finally, there is a wide range of Python-based frameworks with limited functionality or rather rigid data processing pipelines. These frameworks include NeuroPycon (Meunier et al, 2020), Plotly (Plotly Technologies Inc., 2015), matplotlib (Hunter, 2007), HEAR (Kobler et al, 2019), Pygpc (Weise et al, 2020), Human Neocortical Neurosolver (Neymotin et al, 2020), Neo (Marcus et al, 2019), nipype (Gorgolewski et al, 2011), ScoT (Billinger et al, 2014), PyEEG (Bao et al, 2011), Gumpy (Tayeb et al, 2018). Due to either a specific focus on a single topic or limited flexibility, these frameworks were not compared with the framework presented in this study.…”