Independent Component Analysis as a Tool to Eliminate Artifacts in EEG: A Quantitative Study

By Jorge Iriarte, Elena Urrestarazu, Miguel Valencia, M. Alegre, A. Malanda, C. Viteri, Julio Artieda. Journal of clinical neurophysiology, 2003

298 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/0e22bfe09a9bcbeb1d848bb90ebe2d1fe47c496e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Scott Makeig
2. Arnaud Delorme
3. Clemens Brunner
4. Begonya García-Zapirain Soto
5. Jose Antonio Urigüen
6. Robert Leeb
7. Terrence J. Sejnowski
8. Bernhard Graimann

In Signal Processing

1. Scott Makeig
2. Arnaud Delorme
3. Begonya García-Zapirain Soto
4. Jose Antonio Urigüen
5. Terrence J. Sejnowski
6. Thomas Koenig
7. Gert Pfurtscheller
8. Robert Leeb

Citations in the corpus

• Seperability of four-class motor imagery data using independent components analysis (2006, background): “However, since ICA has proven to be robust to outliers and artifacts (Jung et al 2000, James and Gibson 2003, Iriarte et al 2004), none of these trials were precluded from further analysis.”
• Canonical Correlation Analysis Applied to Remove Muscle Artifacts From the Electroencephalogram (2006, methodology): “• ICA: as it is not in the scope of this paper to compare all different ICA algorithms, we limited ourself to the Joint Approximate Diagonalization of Eigen-matrices (JADE) algorithm [12], previously used in two studies for muscle artifact removal in scalp EEG recordings [4], [13].”
• Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis (2007, methodology): “to be an efficient method 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;”
• EEG artifact removal—state-of-the-art and guidelines (2015, methodology): “[23, 43, 68, 70, 147, 148] analyze the results visually and [32]”
• BOLD correlates of EEG alpha phase-locking and the fMRI default mode network (2009, methodology): “If the power inside the scanner clearly exceeded the power outside the scanner then these were identified as MR-related artifacts (Iriarte et al., 2003; Mantini et al., 2007a; Srivastava et al., 2005).”
• Handbook of Blind Source Separation: Independent Component Analysis and Applications (2010, unknown): “46 5”