Topic
Artifact (error)
About: Artifact (error) is a research topic. Over the lifetime, 6212 publications have been published within this topic receiving 119693 citations. The topic is also known as: artefact & artifacts.
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TL;DR: It is demonstrated that the effect of motion on rsfc-MRI can be substantially attenuated through improved preprocessing procedures, but not completely removed.
1,448 citations
TL;DR: It is demonstrated that simultaneous EEG/ fMRI studies are for the first time possible, extending the scope of EEG/fMRI studies considerably.
1,285 citations
TL;DR: In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes and achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively.
1,117 citations
TL;DR: A completely automatic algorithm (ADJUST) that identifies artifacted independent components by combining stereotyped artifact-specific spatial and temporal features is proposed that provides a fast, efficient, and automatic way to use ICA for artifact removal.
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 eventrelated potentials from heavily artifacted data. These results demonstrate that ADJUST provides a fast, efficient, and automatic way to use ICA for artifact removal. Descriptors: Electroencephalography, Independent component analysis, EEG artifacts, EEG artefacts, Event-related potentials, Ongoing brain activity, Automatic classification, Thresholding
1,060 citations
TL;DR: For STA to be compensated for effectively, it is here suggested that either its subject-specific pattern is assessed by ad hoc exercises or it is characterized from a large series of measurements on different subject populations.
1,030 citations