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Independent Component Analysis - an overview ...
jICA is an extension of the popular independent component analysis (ICA). Briefly, ICA is a technique for revealing hidden factors that underlie a set of observable data. ICA has been widely used to solve blind source separation problems (Fig. 16.3); these include, for example, the problem of deriving brain waves recorded using multiple sensors and the problem of removing interfering radio ...
ICA for dummies – Arnaud Delorme
Independent Component Analysis is a signal processing method to separate independent sources linearly mixed in several sensors. For instance, when recording electroencephalograms (EEG) on the scalp, ICA can separate out artifacts embedded in the data (since they are usually independent of each other).
Independent Component Analysis - Papers With Code
Independent component analysis (ICA) is a statistical and computational technique for revealing hidden factors that underlie sets of random variables, measurements, or signals. ICA defines a generative model for the observed multivariate data, which is typically given as a large database of samples. In the model, the data variables are assumed to be linear mixtures of some unknown latent ...
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