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 | |
| 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 |
Fingerprint
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Signal Processing
- Biomedical Engineering
- Health Informatics
Cite this
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
}
TY - GEN
T1 - On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP
AU - Winkler, Irene
AU - Debener, Stefan
AU - Muller, Klaus
AU - Tangermann, Michael
PY - 2015/11/4
Y1 - 2015/11/4
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84953223694&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84953223694&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2015.7319296
DO - 10.1109/EMBC.2015.7319296
M3 - Conference contribution
C2 - 26737196
AN - SCOPUS:84953223694
SN - 9781424492718
VL - 2015-November
SP - 4101
EP - 4105
BT - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
PB - Institute of Electrical and Electronics Engineers Inc.
ER -