Removing electroencephalographic artifacts by blind source separation.

By Tzyy-Ping Jung, Scott Makeig, Colin Humphries, Te-Won Lee, M. Mckeown, V. Iragui, Terrence J. Sejnowski. Psychophysiology, 2000

3,440 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/2587ab802429734bb6d51da884bfd20b4195e9ab

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Citations in the corpus

Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects. (2000, methodology): “Previous results (Makeig et al., 1997, 1999a,b; Jung et al., 2000) and those we report here show that ICA decomposition is relatively insensitive to the exact choice of learning rate or batch size.”
Transient induced gamma-band response in EEG as a manifestation of miniature saccades. (2008, methodology): “For experiment 1, activity due to blinks was removed from the data before segmentation, using the independent component analysis (ICA) method (Jung et al., 2000), as implemented in the Brain Vision Analyzer software .”
Evaluation of Artifact Subspace Reconstruction for Automatic Artifact Components Removal in Multi-Channel EEG Recordings (2020, methodology): “been widely used for separating stereotyped brain processes and various types of artifacts such as muscle, eye-blink, and lateral eye-movement activities [5].”
A Block Coordinate Descent Method for Regularized Multiconvex Optimization with Applications to Nonnegative Tensor Factorization and Completion (2013, background): “It has found applications in many areas such as artifact removal [26] and image processing [28].”
Evaluation of Artifact Subspace Reconstruction for Automatic EEG Artifact Removal (2018, methodology): “ICA has been widely used for separating stereotyped brain processes and various types of artifacts such as muscle, eye-blink, and lateral eye-movement activities [3].”
BCI competition 2003-data set IIb: enhancing P300 wave detection using ICA-based subspace projections for BCI applications (2004, methodology): “According to the spatial distribution of brain activities, an electrode set of interest is selected in which is the index number of electrodes and is equal to the row index of the multichannel EEG matrix .”
Mining event-related brain dynamics. (2004, methodology): “... and clean high-density EEG datasets, can separate out the large or small activities, scalp maps, and scalp data contributions of dozens of maximally independent information sources whose scalp maps fit near-perfectly the dipolar projections of cortical EEG sources [31‐33] (see Figure 1). At the ”
Electrocortical activity is coupled to gait cycle phase during treadmill walking (2011, positive): “We excluded ICs from further analysis if the projection of the equivalent current dipole model to the scalp accounted for less than 80% of the scalp map variance, or if the topography and time-course of the IC was reflective of eye movement artifact (Jung et al., 2000a,b).”
ERPLAB: an open-source toolbox for the analysis of event-related potentials (2014, background): “The presence of this option in the GUI makes it obvious that this option exists, and the label of the option is fairly self-explanatory, obviating the need for the user to consult the documentation.”
Source Separation From Single-Channel Recordings by Combining Empirical-Mode Decomposition and Independent Component Analysis (2010, neutral): “[1]–[3]).”
Mental Training Affects Distribution of Limited Brain Resources (2007, methodology): “Independent component analysis was then used to remove ocular artifacts [27].”
Oscillatory synchronization in large-scale cortical networks predicts perception. (2011, positive): “To reduce remaining artifacts (e.g., small eye movements, muscle twitches, and cardiac artifacts), we applied independent component analysis (Hyvarinen, 1999; Jung et al., 2000), separately for high and low frequencies, and rejected components that reflected signal artifacts.”
Analysis and visualization of single‐trial event‐related potentials (2001, methodology): “Our previous results [Jung et al., 2000a; Makeig et al., 1996, 1997, 1998, 1999a] as well as the results we report here have shown that ICA decomposition is relatively insensitive to the exact choice or learning rate or batch size.”
MNE software for processing MEG and EEG data (2013, methodology): “MNE handles ICA in a similar manner as an SSP operator; the ICA decomposition can be stored in a FIF ﬁ le to be accessed later.”