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Automated artifact removal from the electroencephalogram: a comparative study.

Daly, Ian and Nicolaou, Nicoletta and Nasuto, Slawomir Jaroslaw and Warwick, Kevin (2013) 'Automated artifact removal from the electroencephalogram: a comparative study.' Clinical EEG and Neuroscience, 44 (4). pp. 291-306. ISSN 1550-0594

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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.

Item Type: Article
Uncontrolled Keywords: Brain; Humans; Diagnosis, Computer-Assisted; Electroencephalography; Artifacts; Data Interpretation, Statistical; Sensitivity and Specificity; Reproducibility of Results; Algorithms; Pattern Recognition, Automated; Wavelet Analysis
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 27 May 2021 13:27
Last Modified: 15 Jan 2022 00:53

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