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 |
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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 |
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Country/Territory | 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