OpenViBE Documentation 3.6.0
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This box attempts to find a decomposition of the signal to its
independent components. The approach is based on the FastICA algorithm.
The input signal.
The decomposed signal.
Number of independent components to extract (equals PCA dimension reduction)
Which decomposition is desired?
How many seconds of sample to collect to estimate the ICA model?
Decomposition type. Deflation is an approach where each component is
estimated separately in turns. Symmetric estimation optimizes all
components at once.
Maximum number of iterations
Enable fine tuning?
Maximum number of iterations for the fine tuning
Used nonlinearity type
Mu parameter
Epsilon parameter
Filename to save the estimated decomposition matrix W to
Should the matrix W be saved to a file?
One use-case of ICA is to attempt to separate the signal of interest from
nuisance artifacts. For example, supposing that ICA makes a meaningful decomposition
of your EEG signal, you will see artifacts such as those from eyeblinks more
clearly segregated to specific output channels instead of contaminating
all of the channels.
This plugin applies the FastICA algorithm to the input signal. The box can store the
estimated decomposition matrix W to a file. This file can then be used later
in the spatial filter box to apply the decomposition on fresh data.
The box also outputs the decomposed signal, but the decomposition is active only
after the model has been estimated (after the specified number of samples have been collected).
If you wish to decompose the whole data, then you can first train the ICA model, save the matrix,
and then separately apply it to the original data with the spatial filter.
The FastICA algorithm is described in
A. Hyvarinen. "Fast and Robust Fixed-Point Algorithms for Independent Component Analysis", IEEE Transactions on Neural Networks 10(3):626-634, 1999.
The implementation used by the box is from the ITPP toolkit.