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

Psychophysiology. 2011 Feb;48(2):229-40. doi: 10.1111/j.1469-8986.2010.01061.x.


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.

Keywords: Automatic classification; EEG artefacts; EEG artifacts; Electroencephalography; Event-related potentials; Independent component analysis; Ongoing brain activity; Thresholding.

MeSH terms

  • Adult
  • Algorithms*
  • Artifacts*
  • Auditory Perception / physiology*
  • Electroencephalography / methods*
  • Evoked Potentials / physiology*
  • Humans
  • Visual Perception / physiology*