Seonglae ChoIndependent component analysis
ICA attempts to decompose into independent non-Gaussian components. ICA finds the independent components by maximizing the statistical independence of the estimated components.
The two broadest definitions of independence for ICA are:
- Minimization of Mutual information
- Maximization of non-Gaussianity
Algorithms
- infomax
- FastICA
- JADE
- kernel-ICA
- MELODIC (Multivariate Exploratory Linear Optimized Decomposition into Independent Components)
Application
A typical application of ICA is the “cocktail party problem”, where the underlying speech signal are separated from a sample data consisting of people talking simultaneously in a room since it maximize non-Gaussianity. ICA performs a blind source separation by exploiting the independence and
non-Gaussianity of the original sources. 14.7 Independent Component Analysis and Explor.
ICA has become an important tool in the study of brain dynamics (Brain Wave). ICA has become a widely used method for extracting functional brain networks (regions with significant correlated signal) in the brain during rest and task.
Independent component analysis
In signal processing, independent component analysis (ICA) is a computational method for separating a multivariate signal into additive subcomponents. This is done by assuming that at most one subcomponent is Gaussian and that the subcomponents are statistically independent from each other.[1] ICA was invented by Jeanny Hérault and Christian Jutten in 1985.[2] ICA is a special case of blind source separation. A common example application of ICA is the "cocktail party problem" of listening in on one person's speech in a noisy room.[3]
https://en.wikipedia.org/wiki/Independent_component_analysis
Functional connectivity in mild cognitive impairment with Lewy bodies
Journal of Neurology - Previous resting-state fMRI studies in dementia with Lewy bodies have described changes in functional connectivity in networks related to cognition, motor function, and...
https://link.springer.com/article/10.1007/s00415-021-10580-z
