Independent component approach to the analysis of EEG and MEG recordings

By Ricardo Vigário, J. Särelä, V. Jousmäki, Matti S. Hämäläinen, Erkki Oja. IEEE Transactions on Biomedical Engineering, 2000

855 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b4e282b495fbb3d8c9c9e89465b02108d8f62694

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Citations in the corpus

Removal of Artifacts from EEG Signals: A Review (2019, methodology): “tested the ICA method on simulated and experimental data, and showed good performance in the separation of signals from their linear mixtures and extraction of the eye information present in EOG signals [48,49].”
Mining event-related brain dynamics. (2004, background): “[25,28], MEG [34, 49 ,50] and many other types of data [26].”
Icasso: software for investigating the reliability of ICA estimates by clustering and visualization (2003, background): “In general, Icasso consists of the following steps: 1.”
Default-mode brain dysfunction in mental disorders: a systematic review. (2009, methodology): “ICA has been extensively used for the analysis of electromagnetic brain signals (James and Hesse, 2005; Vigario et al., 2000).”
Handbook of Blind Source Separation: Independent Component Analysis and Applications (2010, neutral): “46 5”
Validating the independent components of neuroimaging time series via clustering and visualization (2004, methodology): “, 2001b) is a general-purpose statistical model that has been widely used in the analysis of brain imaging data (see, e.g., Makeig et al., 1997; McKeown et al., 1998; Vigário et al., 2000).”
EEG artifact removal—state-of-the-art and guidelines (2015, methodology): “In fact, leading researchers in the field have been using ICA and improved variations for the last 15 years—for example Vigário [21, 44, 159, 160], Hÿvarinen [161, 162], Oja [21, 159, 161, 162], James [20, 39, 55, 108], Makeig [43, 52, 97, 102], Jung [43, 52, 97], Comon [41] and Cichocki [83, 163], ”
Independent Component Analysis as a Tool to Eliminate Artifacts in EEG: A Quantitative Study (2003, methodology): “Independent component analysis was recently applied to EEG and magnetoencephalography for research purposes with encouraging results (Ikeda and Toyama, 2000; Maeda et al., 2001; Moritz et al., 2000; Sato et al., 2001; Tong et al., 2001; Vigario et al., 2000).”
BCI competition 2003-data set IIb: enhancing P300 wave detection using ICA-based subspace projections for BCI applications (2004, methodology): “Not very long after the appearance of ICA, it has been broadly applied to biomedical signal and image processing, such as analysis of electrocardiography (ECG) [6], [7], EEG [8], [9], [10], magnetoencephalography (MEG) [11], [12], and functional magnetic resonance imaging (fMRI) [13].”
Imaging brain dynamics using independent component analysis (2001, methodology): “Many other groups, including MEG researchers [34]–[37], have now begun working in this area.”
Autoreject: Automated artifact rejection for MEG and EEG data (2016, methodology): “Then, we removed physiological artifacts (heart beat and eye blinks) using Independent Component Analysis (ICA) (Vig´ario et al., 2000).”
Decomposing Spatiotemporal Brain Patterns into Topographic Latent Sources (2014, positive): “Unsupervised methods, such as independent component analysis (Makeig et al., 1997; Vigário et al., 2000), find projections of the neural data onto a low-dimensional subspace, but ignore covariate information.”