Contents
How is the independent component of an ICA calculated?
ICA finds the independent components (also called factors, latent variables or sources) by maximizing the statistical independence of the estimated components. We may choose one of many ways to define a proxy for independence, and this choice governs the form of the ICA algorithm.
Which is a component of the ICA signal?
The figure below shows a 3-sec portion of the recorded EEG time series and its ICA component activations, the scalp topographies of four selected components, and the artifact-corrected EEG signals obtained by removing four selected EOG and muscle noise components from the data.
What is the output of the ICA matrix?
ICA finds an `unmixing’ matrix, W, which decomposes or linearly unmixes the multi-channel scalp data into a sum of temporally independent and spatially fixed components. The rows of the output data matrix, U = WX, are time courses of activation of the ICA components.
What should go in contents page for ICA students?
The first time you use an acronym you should include the full title eg The Basel Committee on Banking Supervision (BCBS) – from then on you can just include the acronym BCBS. What should go in a contents page?
Which is the best ICA algorithm for dimension reduction?
Whitening and dimension reduction can be achieved with principal component analysis or singular value decomposition. Whitening ensures that all dimensions are treated equally a priori before the algorithm is run. Well-known algorithms for ICA include infomax, FastICA, JADE, and kernel-independent component analysis, among others.
Which is a preprocessing step in the ICA algorithm?
Typical algorithms for ICA use centering (subtract the mean to create a zero mean signal), whitening (usually with the eigenvalue decomposition ), and dimensionality reduction as preprocessing steps in order to simplify and reduce the complexity of the problem for the actual iterative algorithm.
What is the non Gaussianity family of ICA algorithms motivated by?
The non-Gaussianity family of ICA algorithms, motivated by the central limit theorem, uses kurtosis and negentropy .