Automatic removal of eye movement and blink artifacts from EEG data using blind component separation.

By Carrie A. Joyce, I. Gorodnitsky, Marta Kutas. Psychophysiology, 2004

665 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a482ad4f092879f12507fd89f060556fb97da413

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Citations in the corpus

A fully automated correction method of EOG artifacts in EEG recordings. (2007, methodology): “Keywords: Electroencephalogram; Electrooculogram; Artifact processing; Missing values; Regression analysis”
Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis (2007, methodology): “…for separating distinct artifactual processes including eye blink, muscle and electrical artifacts (Barbati et al., 2004; Delorme et al., 2001; Iriarte et al., 2003; James and Gibson, 2003; Joyce et al., 2004; Jung et al., 2000b; Tran et al., 2004; Urrestarazu et al., 2004; Zhukov et al., 2000).”
ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features. (2011, methodology): “Several attempts have been made to guide IC classification by using a number of measures to discriminate artifacted from non-artifacted ICs either in the time domain (Barbati, Porcaro, Zappasodi, Rossini, & Tecchio, 2004; Delorme, Sejnowski, & Makeig, 2007; Mantini, Franciotti, Romani, & Pizzella, 2”
A Review on the Computational Methods for Emotional State Estimation from the Human EEG (2013, background): “As the emotionrelated features have been mostly found in the frontal EEG signal, online algorithms to overcome the eye movement artifact should be continuously developed [97–99].”
Semi-automatic identification of independent components representing EEG artifact. (2009, methodology): “Regarding eye blink artifacts, ICA-based correction compares favourably to more frequently used linear regression procedures (Joyce et al., 2004).”
A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series (2017, methodology): “The same idea is used by EEG practitioners when the EOG channels are kept in the ICA decomposition to better reject EOG artifacts [33].”
Removal of Artifacts from EEG Signals: A Review (2019, methodology): “In order to avoid the errors introduced by manually select components, Joyce et al. [53] introduced an automatic extraction and removal of eye movement artifacts after the ICA analysis.”
Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals (2011, methodology): “The two physiological artifacts most problematic for BCI applications are ocular (EOG) and muscle (EMG) artifacts.”
Handbook of Blind Source Separation: Independent Component Analysis and Applications (2010, neutral): “46 5”
Emotion recognition using brain activity (2008, methodology): “[33] have tried to devise such a method, using the Second Order Blind Inference (sobi) method.”