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Automated Artifact Removal From the Electroencephalogram

Automated Artifact Removal From the Electroencephalogram Contamination of the electroencephalogram (EEG) by artifacts greatly reduces the quality of the recorded signals. There is a need for automated artifact removal methods. However, such methods are rarely evaluated against one another via rigorous criteria, with results often presented based upon visual inspection alone. This work presents a comparative study of automatic methods for removing blink, electrocardiographic, and electromyographic artifacts from the EEG. Three methods are considered; wavelet, blind source separation (BSS), and multivariate singular spectrum analysis (MSSA)-based correction. These are applied to data sets containing mixtures of artifacts. Metrics are devised to measure the performance of each method. The BSS method is seen to be the best approach for artifacts of high signal to noise ratio (SNR). By contrast, MSSA performs well at low SNRs but at the expense of a large number of false positive corrections. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Clinical EEG and Neuroscience SAGE

Automated Artifact Removal From the Electroencephalogram

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References (32)

Publisher
SAGE
Copyright
© EEG and Clinical Neuroscience Society (ECNS) 2013
ISSN
1550-0594
eISSN
2169-5202
DOI
10.1177/1550059413476485
pmid
23666954
Publisher site
See Article on Publisher Site

Abstract

Contamination of the electroencephalogram (EEG) by artifacts greatly reduces the quality of the recorded signals. There is a need for automated artifact removal methods. However, such methods are rarely evaluated against one another via rigorous criteria, with results often presented based upon visual inspection alone. This work presents a comparative study of automatic methods for removing blink, electrocardiographic, and electromyographic artifacts from the EEG. Three methods are considered; wavelet, blind source separation (BSS), and multivariate singular spectrum analysis (MSSA)-based correction. These are applied to data sets containing mixtures of artifacts. Metrics are devised to measure the performance of each method. The BSS method is seen to be the best approach for artifacts of high signal to noise ratio (SNR). By contrast, MSSA performs well at low SNRs but at the expense of a large number of false positive corrections.

Journal

Clinical EEG and NeuroscienceSAGE

Published: Oct 1, 2013

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