Abstract
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 language | English |
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Pages (from-to) | 229-240 |
Number of pages | 12 |
Journal | Psychophysiology |
Volume | 48 |
Issue number | 2 |
DOIs | |
Publication status | Published - Feb 2011 |
Keywords
- 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)