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We applied ICA to the preprocessed data using Group ICA of the fMRI Toolbox (GIFT 4.0a, http://icatb.sourceforge.net/), which utilizes the Infomax algorithm.
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ICA was calculated using the Infomax algorithm implemented in the GIFT Toolbox (http://mialab.mrn.org/software/gift/).org/software/gift/
We are able to show that the FastICA algorithm configured according to ICA architecture II yields the highest performance for identifying faces, while the InfoMax algorithm configured according to ICA architecture II is better for recognizing facial actions.
Subsequently, the ICs were calculated using the Infomax algorithm.
Grouped spatial ICA was performed for all 37 participants (HV + MO) using the infomax algorithm [29].
Second, a group ICA is performed using the Infomax algorithm.
Stage 2: Group estimation of spatially independent sources was then performed using the Infomax algorithm.
In Stage 2, the estimation of independent sources was performed using the Infomax algorithm [70].
Frequently used ICA algorithms to solve the aforementioned linear equation are the Infomax algorithm by Bell and Sejnowski [34] and the Fast ICA algorithm by Hyvärinen [40].
Among them, the infomax algorithm attempts to find the W matrix through maximizing an entropy function as defined in Equation 3 [32], [32].
The Infomax algorithm maximizes the information transferred from inputs through a non-linear neural network transfer function whereas the Fast ICA algorithm maximizes independence among estimated ICs, which is achieved via maximization of negentropy, a measure of deviation from Gaussian distributions.
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