Evaluation of Riemannian Artifact Subspace Reconstruction for the correction of EEG artifacts
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1
University of Oldenburg, Department of Psychology, Germany
Objective: We investigated a method for an effective artifact attenuation in mobile EEG paradigms. Our method is based on Artifact Subspace Reconstruction (ASR), which is designed for online and offline applications to correct artifacts in multichannel EEG data automatically. ASR is an adaptive procedure which repeatedly computes a principal component analysis on covariance matrices of the channel data to detect artifacts based on their statistical properties in the component subspace. We developed and evaluated an adaptation of the existing ASR implementation in which we explored whether use of Riemannian geometry helps to attenuate typical EEG artifacts as well as natural walking related movement artifacts more reliably and with lower computational costs. Methods: Mobile EEG was recorded while participants were sitting and walking outdoors. A small, wireless 24-channel EEG system with gyroscope sensors and a smartphone were used for EEG acquisition and behavioral response recording. We compared artifact-correction sensitivities of the ASR and Riemann-ASR (rASR) methods by analysing the signal to noise ratio (SNR) of eye blinks and natural outdoors walking artifacts. In a second analysis, we evaluated artifact correction specificities by comparing properties of an auditory N100 for response sounds confirming screen touches. Finally, we compared both ASR variants with independent component analysis (ICA), the current gold standard in EEG artifact correction. Results: We found that our rASR implementation reduced computing time significantly compared to ASR and ICA. Moreover, first results suggest that rASR also outperformed ASR with regard to sensitivity and specificity measures, that is, rASR artifact correction resulted in a larger N100 SNR and stronger eye-blink and movement artifact suppression. Conclusions: Our results demonstrate that rASR may be an efficient and objective method for the online correction of multi-channel EEG data acquired during mobile and non-mobile conditions.
References
Mullen, T. R., Kothe, C. A. E., Chi, M., Ojeda, A., Kerth, T., Makeig, S., … Cauwenberghs, G. (2015). Real-time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG. IEEE Transactions on Bio-Medical Engineering, 62(11), 2553–2567. http://doi.org/10.1109/TBME.2015.2481482
Keywords:
Artifact correction,
Signal processing algorithms,
EEG,
mobile EEG,
Online Systems
Conference:
2nd International Neuroergonomics Conference, Philadelphia, PA, United States, 27 Jun - 29 Jun, 2018.
Presentation Type:
Poster Presentation
Topic:
Neuroergonomics
Citation:
Blum
S,
Bleichner
MG and
Debener
S
(2019). Evaluation of Riemannian Artifact Subspace Reconstruction for the correction of EEG artifacts.
Conference Abstract:
2nd International Neuroergonomics Conference.
doi: 10.3389/conf.fnhum.2018.227.00134
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Received:
26 Mar 2018;
Published Online:
27 Sep 2019.
*
Correspondence:
Mrs. Sarah Blum, University of Oldenburg, Department of Psychology, Oldenburg, Germany, sarah.blum@uol.de