Independent Component Analysis for Identification of Artifacts in Magnetoencephalographic Recordings

By Ricardo Vigário, V. Jousmäki, Matti S. Hämäläinen, Riitta Hari, Erkki Oja. 1997

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Citations in the corpus

ICA of fMRI Group Study Data (2002, unknown): “, 1996) and MEG (Vigário et al., 1998) data.”
Artifact Correction of the Ongoing EEG Using Spatial Filters Based on Artifact and Brain Signal Topographies (2002, methodology): “, 1997), independent component analysis (ICA) (Jung et al. 1998, 2000a, b; Vigário et al., 1998), the multiple source eye correction (MSEC) approach (Berg and Scherg, 1991a, b, 1994), the preselection method (Ille, 2001; Ille et al.”
Removal of magnetoencephalographic artifacts with temporal signal‐space separation: Demonstration with single‐trial auditory‐evoked responses (2009, unknown): “Previously, independent component analysis (ICA) has been used successfully to remove eye-related artifacts [Vigário et al., 1997], and comparison of tSSS with ICA would be of interest in the future.”
Independent component approach to the analysis of EEG and MEG recordings (2000, background): “Its main applications are in feature extraction [12], [25], and blind source separation (BSS) [2], [8], with special emphasis to physiological data analysis [29], [31], [33], [19], [3], [23], [34], [17], and audio signal processing [28].”
Validating the independent components of neuroimaging time series via clustering and visualization (2004, methodology): “These components may not look very interesting at first sight, which is possibly why they were not investigated in detail by Vigário et al. (1998).”
Handbook of Blind Source Separation: Independent Component Analysis and Applications (2010, unknown): “46 5”
Icasso: software for investigating the reliability of ICA estimates by clustering and visualization (2003, methodology): “We experimented with a biomedical benchmarking data set described in more detail in [19].”
Fast and robust fixed-point algorithms for independent component analysis (1999, methodology): “These applications include artifact ca cellation in EEG and MEG [36], [37], decomposition of evoked fields in MEG [38], and feature extraction of image data [25], [35].”
Efficient independent component analysis (2006, methodology): “For example, Vigario, Jousmaki, Hamalainen, Hari and Oja [25] used ICA to separate artifacts from magnetoencephalography (MEG) data, without the burden of modeling the process that generated the artifacts.”
Adaptive blind signal and image processing (2002, methodology): “435 Preface Signal Processing has always played a critical role in science and technology and development of new systems like computer tomography, (PET, fMRI, EEG/MEG, optical recordings), wireless communications, digital cameras, HDTV, etc.”