ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features |
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| Authors: | Andrea Mognon Jorge Jovicich Lorenzo Bruzzone Marco Buiatti |
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| Affiliation: | 1. Functional NeuroImaging Laboratory, Center for Mind/Brain Sciences, Department of Cognitive and Education Sciences, University of Trento, Trento, Italy;2. NILab, Neuroinformatics Laboratory, Fondazione Bruno Kessler, Trento, Italy;3. Department of Information Engineering and Computer Science, University of Trento, Trento, Italy;4. INSERM, U992, Cognitive Neuroimaging Unit, Gif/Yvette, France;5. CEA, DSV/I2BM, NeuroSpin Center, Gif/Yvette, France;6. Université Paris‐Sud, Cognitive Neuroimaging Unit, Gif/Yvette, France |
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| 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. |
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| Keywords: | Electroencephalography Independent component analysis EEG artifacts EEG artefacts Event‐related potentials Ongoing brain activity Automatic classification Thresholding |
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