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

Publikation: KonferenzbeitragPosterForschung

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.
Originalspracheenglisch
Seitenumfang1
PublikationsstatusVeröffentlicht - 26 Jul 2019
Veranstaltung41st International Engineering in Medicine and Biology Conference 2019 - Berlin, Deutschland
Dauer: 23 Jul 201927 Jul 2019
Konferenznummer: 41
https://embc.embs.org/2019/

Konferenz

Konferenz41st International Engineering in Medicine and Biology Conference 2019
KurztitelIEEE EMBC 2019
LandDeutschland
OrtBerlin
Zeitraum23/07/1927/07/19
Internetadresse

Fingerprint

Electrodes
Brain
Functional neuroimaging
Feedback
Ions

Fields of Expertise

  • Human- & Biotechnology

Dies zitieren

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. Postersitzung präsentiert bei 41st International Engineering in Medicine and Biology Conference 2019, Berlin, Deutschland.

HEAR to remove pops and drifts: the high-variance electrode artifact removal (HEAR) algorithm. / Kobler, Reinmar; Sburlea, Andreea Ioana; Mondini, Valeria; Müller-Putz, Gernot.

2019. Postersitzung präsentiert bei 41st International Engineering in Medicine and Biology Conference 2019, Berlin, Deutschland.

Publikation: KonferenzbeitragPosterForschung

Kobler R, Sburlea AI, Mondini V, Müller-Putz G. HEAR to remove pops and drifts: the high-variance electrode artifact removal (HEAR) algorithm. 2019. Postersitzung präsentiert bei 41st International Engineering in Medicine and Biology Conference 2019, Berlin, Deutschland.
Kobler, Reinmar ; Sburlea, Andreea Ioana ; Mondini, Valeria ; Müller-Putz, Gernot. / HEAR to remove pops and drifts: the high-variance electrode artifact removal (HEAR) algorithm. Postersitzung präsentiert bei 41st International Engineering in Medicine and Biology Conference 2019, Berlin, Deutschland.1 S.
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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.",
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AU - Mondini, Valeria

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N2 - 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.

AB - 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.

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