Independent component analysis

As you can guess from the name, independent component analysis (ICA) is an approach where you try to derive independent components from the input signal. In fact, ICA is a technique that allows you to create maximally independent additive subcomponents from the initial multivariate input signal. The main hypothesis of this technique focuses on the statistical independence of the subcomponents and their non-Gaussian distribution. ICA has a lot of applications in neurological data and is widely used in the neuroscience domain.

A typical scenario that may require the use of ICA is blind source separation. For example, two or more microphones will record two sounds (for instance, a person speaks and a song plays ...

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