Blum, Sarah and Jacobsen, Nadine S. J. and Bleichner, Martin G. and Debener, Stefan (2019) A Riemannian Modification of Artifact Subspace Reconstruction for EEG Artifact Handling. Frontiers in Human Neuroscience, 13. ISSN 1662-5161

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Artifact Subspace Reconstruction (ASR) is an adaptive method for the online or offline correction of artifacts comprising multichannel electroencephalography (EEG) recordings. It repeatedly computes a principal component analysis (PCA) on covariance matrices to detect artifacts based on their statistical properties in the component subspace. We adapted the existing ASR implementation by using Riemannian geometry for covariance matrix processing. EEG data that were recorded on smartphone in both outdoors and indoors conditions were used for evaluation (N = 27). A direct comparison between the original ASR and Riemannian ASR (rASR) was conducted for three performance measures: reduction of eye-blinks (sensitivity), improvement of visual-evoked potentials (VEPs) (specificity), and computation time (efficiency). Compared to ASR, our rASR algorithm performed favorably on all three measures. We conclude that rASR is suitable for the offline and online correction of multichannel EEG data acquired in laboratory and in field conditions.

Item Type: Article
Additional Information: Publiziert mit Hilfe des DFG-geförderten Open Access-Publikationsfonds der Carl von Ossietzky Universität Oldenburg.
Uncontrolled Keywords: Riemann, mobile EEG, ASR, BCI, neuroergonomics, PCA, PGA
Subjects: Philosophy and psychology > Psychology
Divisions: Faculty of Medicine and Health Sciences > Department of Psychology
Date Deposited: 18 Mar 2020 08:10
Last Modified: 18 Mar 2020 08:29
URN: urn:nbn:de:gbv:715-oops-45446
DOI: 10.3389/fnhum.2019.00141

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