On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP

Irene Winkler; Stefan Debener; Klaus Muller; Michael Tangermann

doi:10.1109/EMBC.2015.7319296

On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP

Irene Winkler, Stefan Debener, Klaus Muller, Michael Tangermann

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

99 Citations (Scopus)

Abstract

Standard artifact removal methods for electroencephalographic (EEG) signals are either based on Independent Component Analysis (ICA) or they regress out ocular activity measured at electrooculogram (EOG) channels. Successful ICA-based artifact reduction relies on suitable pre-processing. Here we systematically evaluate the effects of high-pass filtering at different frequencies. Offline analyses were based on event-related potential data from 21 participants performing a standard auditory oddball task and an automatic artifactual component classifier method (MARA). As a pre-processing step for ICA, high-pass filtering between 1-2 Hz consistently produced good results in terms of signal-to-noise ratio (SNR), single-trial classification accuracy and the percentage of 'near-dipolar' ICA components. Relative to no artifact reduction, ICA-based artifact removal significantly improved SNR and classification accuracy. This was not the case for a regression-based approach to remove EOG artifacts.

Original language	English
Title of host publication	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4101-4105
Number of pages	5
Volume	2015-November
ISBN (Print)	9781424492718
DOIs	https://doi.org/10.1109/EMBC.2015.7319296
Publication status	Published - 2015 Nov 4
Externally published	Yes
Event	37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 - Milan, Italy Duration: 2015 Aug 25 → 2015 Aug 29

Other

Other	37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Country	Italy
City	Milan
Period	15/8/25 → 15/8/29

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Signal Processing
Biomedical Engineering
Health Informatics

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Winkler, I., Debener, S., Muller, K., & Tangermann, M. (2015). On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (Vol. 2015-November, pp. 4101-4105). [7319296] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2015.7319296

On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP. / Winkler, Irene; Debener, Stefan; Muller, Klaus; Tangermann, Michael.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. Vol. 2015-November Institute of Electrical and Electronics Engineers Inc., 2015. p. 4101-4105 7319296.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Winkler, I, Debener, S, Muller, K & Tangermann, M 2015, On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP. in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. vol. 2015-November, 7319296, Institute of Electrical and Electronics Engineers Inc., pp. 4101-4105, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015, Milan, Italy, 15/8/25. https://doi.org/10.1109/EMBC.2015.7319296

Winkler I, Debener S, Muller K, Tangermann M. On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. Vol. 2015-November. Institute of Electrical and Electronics Engineers Inc. 2015. p. 4101-4105. 7319296 https://doi.org/10.1109/EMBC.2015.7319296

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abstract = "Standard artifact removal methods for electroencephalographic (EEG) signals are either based on Independent Component Analysis (ICA) or they regress out ocular activity measured at electrooculogram (EOG) channels. Successful ICA-based artifact reduction relies on suitable pre-processing. Here we systematically evaluate the effects of high-pass filtering at different frequencies. Offline analyses were based on event-related potential data from 21 participants performing a standard auditory oddball task and an automatic artifactual component classifier method (MARA). As a pre-processing step for ICA, high-pass filtering between 1-2 Hz consistently produced good results in terms of signal-to-noise ratio (SNR), single-trial classification accuracy and the percentage of 'near-dipolar' ICA components. Relative to no artifact reduction, ICA-based artifact removal significantly improved SNR and classification accuracy. This was not the case for a regression-based approach to remove EOG artifacts.",

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On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP

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