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

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 combin...

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Bibliographic Details
Published inPsychophysiology Vol. 48; no. 2; pp. 229 - 240
Main Authors Mognon, Andrea, Jovicich, Jorge, Bruzzone, Lorenzo, Buiatti, Marco
Format Journal Article
LanguageEnglish
Published Malden, USA Blackwell Publishing Inc 01.02.2011
Blackwell Publishing Ltd
Subjects
Adult
Algorithms
Auditory Perception - physiology
Automatic classification
Classification
Computational mathematics
Data processing
EEG
EEG artefacts
EEG artifacts
Electroencephalography
Electroencephalography - methods
Event-related potentials
Evoked Potentials - physiology
Eye
Humans
Independent component analysis
Joints
Ongoing brain activity
Physiological psychology
Sensory integration
Temporal variations
Thresholding
Visual Perception - physiology
Automatic classification
EEG artifacts
EEG artefacts
Thresholding
Independent component analysis
Event-related potentials
Electroencephalography
Ongoing brain activity
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Summary: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.
Bibliography:ArticleID:PSYP1061
istex:B32DA8FE3C28E6DCD51D97F105D706ED214D0C7B
ark:/67375/WNG-K68524L6-H
We thank Mariano Sigman and Stanislas Dehaene for sharing the EEG data, Francesca Bovolo and Michele Dalponte for helpful advice on the use of the thresholding algorithm, and Sara Assecondi for valuable comments on an earlier version of the manuscript.
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ISSN:0048-5772
1469-8986
1469-8986
1540-5958
DOI:10.1111/j.1469-8986.2010.01061.x