Journal
CLINICAL EEG AND NEUROSCIENCE
Volume 44, Issue 4, Pages 291-306Publisher
SAGE PUBLICATIONS INC
DOI: 10.1177/1550059413476485
Keywords
Automated artifact removal; Independent component analysis (ICA); Temporal de-correlation source separation (TDSEP); Blind source separation (BSS); Multivariate singular spectrum analysis (MSSA); Wavelets
Funding
- Engineering and Physical Sciences Research Council [EP/J003077/1] Funding Source: researchfish
- EPSRC [EP/J003077/1] Funding Source: UKRI
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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.
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