Imaging brain dynamics using independent component analysis

By Tzyy-Ping Jung, Scott Makeig, Martin J. McKeown, Anthony J. Bell, Te-Won Lee, Terrence J. Sejnowski. Proceedings of the IEEE, 2001

619 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/fbde910943e25da234e647864f0c76a0646a0a1f

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Citations in the corpus

EEG Signal Analysis: A Survey (2010, methodology): “Independent component analysis (ICA) is an effective method for removing artifacts and separating individual sources of brain signals from EEG recordings [ 87 ].”
Network Oscillations: Emerging Computational Principles (2006, methodology): “New techniques for analyzing single-trial event-related potentials and functional magnetic resonance signals may help us to eavesdrop on the communications system of the brain (Jung et al., 2001; Laughlin and Sejnowski, 2003; McKeown et al., 2003).”
Analysing concurrent transcranial magnetic stimulation and electroencephalographic data: A review and introduction to the open‐source TESA software (2017, neutral): “Removing other artifacts caused by TMS In addition to removing large amplitude artifacts, ICA is also used to remove other artifacts such as eye movements and blinks, persistent muscle activity and electrode noise (Jung et al., 2001; Onton et al., 2006).”
Analysis and visualization of single‐trial event‐related potentials (2001, methodology): “The “ERP image” is a new method for visualizing variability in the latencies and amplitudes of event-related responses [Jung et al., 1999, 2000b, 2001].”
Automated EEG analysis of epilepsy: A review (2013, methodology): “ICA can be effectively used to remove artifacts and to decompose EEG recorded signals into different component signals originated from different sources [57].”
Mining event-related brain dynamics. (2004, methodology): “[25,28], MEG [34,49,50] and many other types of data [ 26 ].”
Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis (2007, methodology): “Independent component analysis (ICA) (Bell and Sejnowski, 1995; Jung et al., 2001; Makeig et al., 1996) applied to concatenated collections of single-trial EEG data has also proven”
Removing artefacts from TMS-EEG recordings using independent component analysis: Importance for assessing prefrontal and motor cortex network properties (2014, methodology): “, 2010) as well as separating independent cortical sources from EEG recordings (Jung et al., 2001; Makeig et al., 1997; Onton et al., 2006).”
Frontal midline EEG dynamics during working memory (2005, background): “The rows of the resulting matrix u are the independent component activities or activations and its columns the time points of the input data (Jung et al., 2001; Makeig et al., 2002).”
Imaging human EEG dynamics using independent component analysis. (2006, background): “Jung et al. (2001) reviewed how these and other methods may all be derived from a common information theoretic framework.”