ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features

Andrea Mognon, Jorge Jovicich, Lorenzo Bruzzone, Marco Buiatti

Research output: Contribution to journalArticlepeer-review


A successful method for removing artifacts from electroencephalogram (EEG) recordings is Independent Component Analysis (ICA), but its implementation remains largely user-dependent. Here, we propose a completely automatic algorithm (ADJUST) that identifies artifacted independent components by combining stereotyped artifact-specific spatial and temporal features. Features were optimized to capture blinks, eye movements, and generic discontinuities on a feature selection dataset. Validation on a totally different EEG dataset shows that (1) ADJUST's classification of independent components largely matches a manual one by experts (agreement on 95.2% of the data variance), and (2) Removal of the artifacted components detected by ADJUST leads to neat reconstruction of visual and auditory event-related potentials from heavily artifacted data. These results demonstrate that ADJUST provides a fast, efficient, and automatic way to use ICA for artifact removal.

Original languageEnglish
Pages (from-to)229-240
Number of pages12
Issue number2
Publication statusPublished - Feb 2011


  • Automatic classification
  • EEG artefacts
  • EEG artifacts
  • Electroencephalography
  • Event-related potentials
  • Independent component analysis
  • Ongoing brain activity
  • Thresholding

ASJC Scopus subject areas

  • Physiology
  • Physiology (medical)
  • Experimental and Cognitive Psychology
  • Neuropsychology and Physiological Psychology
  • Medicine(all)


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