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

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

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
TitelProceedings of the 41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Herausgeber (Verlag)IEEE Xplore
Seitenumfang6
PublikationsstatusVeröffentlicht - 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

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Electrodes
Brain
Functional neuroimaging
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Schlagwörter

    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. in Proceedings of the 41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) IEEE Xplore.

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

    Proceedings of the 41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE Xplore, 2019.

    Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

    Kobler, R, Sburlea, AI, Mondini, V & Müller-Putz, G 2019, HEAR to remove pops and drifts: the high-variance electrode artifact removal (HEAR) algorithm. in Proceedings of the 41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE Xplore, Berlin, Deutschland, 23/07/19.
    Kobler R, Sburlea AI, Mondini V, Müller-Putz G. HEAR to remove pops and drifts: the high-variance electrode artifact removal (HEAR) algorithm. in Proceedings of the 41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE Xplore. 2019
    Kobler, Reinmar ; Sburlea, Andreea Ioana ; Mondini, Valeria ; Müller-Putz, Gernot. / HEAR to remove pops and drifts: the high-variance electrode artifact removal (HEAR) algorithm. Proceedings of the 41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE Xplore, 2019.
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    title = "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 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.",
    keywords = "Electroencephalogram (EEG), Artifacts, electrode sclap interface, brain-computer interface",
    author = "Reinmar Kobler and Sburlea, {Andreea Ioana} and Valeria Mondini and Gernot M{\"u}ller-Putz",
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    AU - Kobler, Reinmar

    AU - Sburlea, Andreea Ioana

    AU - Mondini, Valeria

    AU - Müller-Putz, Gernot

    PY - 2019/7

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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.

    KW - Electroencephalogram (EEG)

    KW - Artifacts

    KW - electrode sclap interface

    KW - brain-computer interface

    M3 - Conference contribution

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