Automated artifact removal from the electroencephalogram: A comparative study

Ian Daly, Nicoletta Nicolaou, Slawomir Jaroslaw Nasuto, Kevin Warwick

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)291-306
Number of pages16
JournalClinical EEG and Neuroscience
Volume44
Issue number4
DOIs
Publication statusPublished - 1 Oct 2013
Externally publishedYes

Keywords

  • Automated artifact removal
  • Blind source separation (BSS)
  • Independent component analysis (ICA)
  • Multivariate singular spectrum analysis (MSSA)
  • Temporal de-correlation source separation (TDSEP)
  • Wavelets

Fingerprint

Dive into the research topics of 'Automated artifact removal from the electroencephalogram: A comparative study'. Together they form a unique fingerprint.

Cite this