Independent Component Analysis Removing Artifacts in Ictal Recordings
نویسندگان
چکیده
منابع مشابه
Independent Component Analysis for Identification of Artifacts in Magnetoencephalographic Recordings
We have studied the application of an independent component analysis (ICA) approach to the identification and possible removal of artifacts from a magnetoencephalographic (MEG) recording. This statistical technique separates components according to the kurtosis of their amplitude distributions over time, thus distinguishing between strictly periodical signals, and regularly and irregularly occu...
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In this work, we deal with the elimination of artifacts (electrodes, muscle, respiration, etc.) from the electrocardiographic (ECG) signal. We use a new tool called independent component analysis (ICA) that blindly separates mixed statistically independent signals. ICA can separate the signal from the interference, even if both overlap in frequency. In order to estimate the mixing parameters in...
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In this paper, we propose a nonparametric rank-based alternative to the least-squares independent component analysis algorithm developed. The basic idea is to estimate the squared-loss mutual information, which used as the objective function of the algorithm, based on its copula density version. Therefore, no marginal densities have to be estimated. We provide empirical evaluation of th...
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OBJECTIVE The contamination of muscle and eye artifacts during an ictal period of the EEG significantly distorts source estimation algorithms. Recent blind source separation (BSS) techniques based on canonical correlation (BSS-CCA) and independent component analysis with spatial constraints (SCICA) have shown much promise in the removal of these artifacts. In this study we want to use BSS-CCA a...
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ژورنال
عنوان ژورنال: Epilepsia
سال: 2004
ISSN: 0013-9580,1528-1167
DOI: 10.1111/j.0013-9580.2004.12104.x