Howtosignalprocessing
- Why Gaussian variables are forbidden for ICA?
- What is the difference between PCA and ICA?
- What is ICA in EEG?
- What is ICA in statistics?
Why Gaussian variables are forbidden for ICA?
I know it's commonly asked why Gaussians are forbidden from use in independent components analysis. This is because a gaussian source distribution will result in the same observed distribution no matter what the mixing matrix A is.
What is the difference between PCA and ICA?
While the goal in PCA is to find an orthogonal linear transformation that maximizes the variance of the variables, the goal of ICA is to find the linear transformation, which the basis vectors are statistically independent and non-Gaussian.
What is ICA in EEG?
Independent Component Analysis (ICA) is often used at the signal preprocessing stage in EEG analysis for its ability to filter out artifacts from the signal. The benefits of using ICA are the most apparent when multi-channel signal is recorded.
What is ICA in statistics?
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.
