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  • Autor
    • Steyrl, David
    • Krausz, Gunther
    • Koschutnig, Karl
    • Edlinger, Günther
    • Müller-Putz, Gernot
  • TitelReference layer adaptive filtering (RLAF) for EEG artifact reduction in simultaneous EEG-fMRI
  • Datei
  • Persistent Identifier
  • Erschienen inJournal of neural engineering
  • Band14
  • Erscheinungsjahr2016
  • Heft2
  • Seiten1-20
  • LicenceCC-BY
  • ZugriffsrechteCC-BY
  • Download Statistik470
  • Peer ReviewJa
  • AbstractObjective: Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) combines advantages of both methods, namely high temporal resolution of EEG and high spatial resolution of fMRI. However, EEG quality is limited due to severe artifacts caused by fMRI scanners. Approach: To improve EEG data quality substantially, we introduce methods that use a reusable reference layer EEG cap prototype in combination with adaptive filtering. The first method, reference layer adaptive filtering (RLAF), uses adaptive filtering with reference layer artifact data to optimize artifact subtraction from EEG. In the second method, multi band reference layer adaptive filtering (MBRLAF), adaptive filtering is performed on bandwidth limited sub-bands of the EEG and the reference channels. Main Results: The results suggests that RLAF outperforms the baseline method, average artifact subtraction, in all settings and also its direct predecessor, reference layer artifact subtraction (RLAS), in lower (<35Hz) frequency ranges. MBRLAF is computationally more demanding than RLAF, but highly effective in all EEG frequency ranges. Effectivity is determined by visual inspection, as well as root-mean-square voltage reduction and power reduction of EEG provided that physiological EEG components such as occipital EEG alpha power and visual evoked potentials (VEP) are preserved. We demonstrate that both, RLAF and MBRLAF, improve VEP quality. For that, we calculate the mean-squared-distance of single trial VEP to the mean VEP and estimate single trial VEP classification accuracies. We found that the average meansquared- distance is lowest and the average classification accuracy is highest after MBLAF. RLAF was second best. Significance: In conclusion, the results suggests that RLAF and MBRLAF are potentially very effective in improving EEG quality of simultaneous EEG-fMRI.