Detection and removal of muscle artifacts from scalp EEG recordings in patients with epilepsy

Maria Anastasiadou, Avgis Hadjipapas, Manolis Christodoulakis, Eleftherios S. Papathanasiou, Savvas S. Papacostas, Georgios D. Mitsis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)

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 languageEnglish
Title of host publicationProceedings - IEEE 14th International Conference on Bioinformatics and Bioengineering, BIBE 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages291-296
Number of pages6
ISBN (Electronic)9781479975013
DOIs
Publication statusPublished - 5 Feb 2014
Event14th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2014 - Boca Raton, United States
Duration: 10 Nov 201412 Nov 2014

Other

Other14th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2014
Country/TerritoryUnited States
CityBoca Raton
Period10/11/1412/11/14

Keywords

  • Blind Source Separation
  • Canonical Correlation Analysis
  • EEG
  • Epilepsy
  • Muscle Artifacts

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