Reference layer adaptive filtering (RLAF) in simultaneous EEG-fMRI

David Steyrl, Gernot Müller-Putz

Research output: Contribution to conferencePosterResearchpeer-review

Abstract

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) combines advantages of both methods: high temporal resolution of EEG and high spatial resolution of fMRI. EEG recordings are, however, afflicted by severe artifacts caused by fMRI scanners. Average artifact subtraction (AAS) is a common method to reduce those artifacts. Recently, we introduced an add-on method that uses a reusable reference layer EEG cap prototype in combination with adaptive filtering, to improve EEG data quality substantially. The methods applies adaptive filtering with reference layer artefact data to optimize artefact subtraction from EEG and is named reference layer adaptive filtering (RLAF).
Original languageEnglish
Publication statusPublished - 3 Nov 2017
Event3rd Alpine Chapter Symposium of the OHBM - Inselspital, Bern, Switzerland
Duration: 3 Nov 20174 Nov 2017

Conference

Conference3rd Alpine Chapter Symposium of the OHBM
CountrySwitzerland
CityBern
Period3/11/174/11/17

Fields of Expertise

  • Human- & Biotechnology

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  • Cite this

    Steyrl, D., & Müller-Putz, G. (2017). Reference layer adaptive filtering (RLAF) in simultaneous EEG-fMRI. Poster session presented at 3rd Alpine Chapter Symposium of the OHBM, Bern, Switzerland.