Automatic artifact rejection from multichannel scalp EEG by wavelet ICA

Nadia Mammone, Fabio La Foresta, Francesco Carlo Morabito

Research output: Contribution to journalArticlepeer-review


Electroencephalographic (EEG) recordings are often contaminated by artifacts, i.e., signals with noncerebral origin that might mimic some cognitive or pathologic activity, this way affecting the clinical interpretation of traces. Artifact rejection is, thus, a key analysis for both visual inspection and digital processing of EEG. Automatic artifact rejection is needed for effective real time inspection because manual rejection is time consuming. In this paper, a novel technique (Automatic Wavelet Independent Component Analysis, AWICA) for automatic EEG artifact removal is presented. Through AWICA we claim to improve the performance and fully automate the process of artifact removal from scalp EEG. AWICA is based on the joint use of the Wavelet Transform and of ICA: it consists of a two-step procedure relying on the concepts of kurtosis and Renyi's entropy. Both synthesized and real EEG data are processed by AWICA and the results achieved were compared to the ones obtained by applying to the same data the "wavelet enhanced" ICA method recently proposed by other authors. Simulations illustrate that AWICA compares favorably to the other technique. The method here proposed is shown to yield improved success in terms of suppression of artifact components while reducing the loss of residual informative data, since the components related to relevant EEG activity are mostly preserved.

Original languageEnglish
Article number5713804
Pages (from-to)533-542
Number of pages10
JournalIEEE Sensors Journal
Issue number3
Publication statusPublished - 2012


  • Electroencephalographic (EEG) artifacts
  • entropy
  • independent component analysis
  • kurtosis
  • wavelet

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Instrumentation


Dive into the research topics of 'Automatic artifact rejection from multichannel scalp EEG by wavelet ICA'. Together they form a unique fingerprint.

Cite this