Abstract
The Electroencephalogram (EEG) is often contaminated by muscle artifacts. EEG is a widely used recording technique for the study of many brain related diseases such as epilepsy. The detection and removal of muscle artifacts from the EEG signal poses a real challenge and is crucial for the reliable interpretation of EEG-based quantitative measures. In this paper, an automatic method for detection and removal of muscle artifacts from scalp EEG recordings, based on canonical correlation analysis (CCA), is introduced. To this end we exploit the fact that the EEG signal may exhibit altered autocorrelation structure and spectral characteristics during periods when it is contaminated by muscle activity. Therefore, we design classifiers in order to automatically discriminate between contaminated and non-contaminated EEG epochs using features based on the aforementioned quantities and examine their performance on simulated data and in scalp EEG recordings obtained from patients with epilepsy.
Original language | English |
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Title of host publication | Proceedings - IEEE 14th International Conference on Bioinformatics and Bioengineering, BIBE 2014 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 291-296 |
Number of pages | 6 |
ISBN (Electronic) | 9781479975013 |
DOIs | |
Publication status | Published - 5 Feb 2014 |
Event | 14th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2014 - Boca Raton, United States Duration: 10 Nov 2014 → 12 Nov 2014 |
Other
Other | 14th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2014 |
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Country/Territory | United States |
City | Boca Raton |
Period | 10/11/14 → 12/11/14 |
Keywords
- Blind Source Separation
- Canonical Correlation Analysis
- EEG
- Epilepsy
- Muscle Artifacts