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

Research output: Contribution to conferencePosterResearch

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.
Original languageEnglish
Number of pages1
Publication statusPublished - 26 Jul 2019
Event41st International Engineering in Medicine and Biology Conference 2019 - Berlin, Germany
Duration: 23 Jul 201927 Jul 2019
Conference number: 41
https://embc.embs.org/2019/

Conference

Conference41st International Engineering in Medicine and Biology Conference 2019
Abbreviated titleIEEE EMBC 2019
CountryGermany
CityBerlin
Period23/07/1927/07/19
Internet address

Fields of Expertise

  • Human- & Biotechnology

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    Kobler, R., Sburlea, A. I., Mondini, V., & Müller-Putz, G. (2019). HEAR to remove pops and drifts: the high-variance electrode artifact removal (HEAR) algorithm. Poster session presented at 41st International Engineering in Medicine and Biology Conference 2019, Berlin, Germany.