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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].
Independent Component Analysis (ICA) was performed using the infomax algorithm (Bell and Sejnowski, 1995) implemented in EEGLAB for advanced noise removing purposes.
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In the second stage of the analysis (2), we used the Infomax algorithm to run the appropriate ICA and a mask based on all subjects.
The group independent components (ICs) were estimated using the Infomax approach [38], and each component was represented by a spatial map and a temporal profile.
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].
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.
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.
More suggestions(13)
using the targetscan algorithm
using the clustalw algorithm
using the bionj algorithm
using the amares algorithm
using the de algorithm
using the david algorithm
using the tango algorithm
using the hungarian algorithm
using the upgma algorithm
using the sam algorithm
using the rma algorithm
using the sequest algorithm
using the viterbi algorithm
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