Imaging human EEG dynamics using independent component analysis.

By Julie Anne Onton, Marissa Westerfield, Jeanne Townsend, Scott Makeig. Neuroscience and Biobehavioral Reviews, 2006

707 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/d09d6b2f59eb59cb21c3f88d46f9d754e4698a50

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Citations in the corpus

Tracking whole-brain connectivity dynamics in the resting state. (2014, neutral): “Keywords: Dynamics, fMRI, Functional connectivity, Independent component analysis, Intrinsic activity, Resting state, State variability”
Removing artefacts from TMS-EEG recordings using independent component analysis: Importance for assessing prefrontal and motor cortex network properties (2014, methodology): “…(ICs) by separating the measured data matrix into two new matrices; a mixing matrix, whose columns are the topographies of the hidden sources, and a matrix of time courses, whose rows are timedependent amplitudes of the sources (Hyvärinen and Oja, 2000; Makeig et al., 1997; Onton et al., 2006).”
Semi-automatic identification of independent components representing EEG artifact. (2009, methodology): “, 2006); or the removal of residual ballistocardiogram and magnetic resonance imaging (MRI) gradient artifact from EEG data recorded inside the MRI (Debener et al., 2007, 2008b; Eichele et al., 2005; Feige et al., 2005; Onton et al., 2006).”
Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis (2017, background): “Scalp EEG data recorded at a single electrode is generally considered as a simple sum of multiple time series from different cortical sources [20].”
Analysing concurrent transcranial magnetic stimulation and electroencephalographic data: A review and introduction to the open‐source TESA software (2017, positive): “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).”
Evaluation of Artifact Subspace Reconstruction for Automatic Artifact Components Removal in Multi-Channel EEG Recordings (2020, methodology): “With this assumption, it is reasonable to model each brain component in EEG recordings as an equivalent current dipole in the cortex [40].”
Transfer Learning in Brain-Computer Interfaces (2015, methodology): “To eliminate artifacts, independent component analysis (ICA) was performed on each session using the SOBI algorithm [38] and components corresponding to cortical features [39], [40] were manually chosen.”
Level of participation in robotic-assisted treadmill walking modulates midline sensorimotor EEG rhythms in able-bodied subjects (2012, methodology): “Before submitting the EEG data to an ICA decomposition the data had to be preprocessed accordingly and was subjected to a rigorous artifact rejection following the methods of Gwin et al. (2010) and Onton et al. (2006).”
Electrocortical activity is coupled to gait cycle phase during treadmill walking (2011, positive): “Independent component analysis (ICA) combined with magnetic resonance based head models can be used to overcome electromyographic, electroocular, movement artifact, and line noise contamination of EEG signals (Delorme and Makeig, 2004; Delorme et al., 2007; Gwin et al., 2010; Jung et al., 2000a,b; M”