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.
|Number of pages||1|
|Publication status||Published - 26 Jul 2019|
|Event||41st International Engineering in Medicine and Biology Conference 2019 - Berlin, Germany|
Duration: 23 Jul 2019 → 27 Jul 2019
Conference number: 41
|Conference||41st International Engineering in Medicine and Biology Conference 2019|
|Abbreviated title||IEEE EMBC 2019|
|Period||23/07/19 → 27/07/19|
Fields of Expertise
- Human- & Biotechnology
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.