Journal: BioMed Research International

Article Title: A Review of Feature Extraction Software for Microarray Gene Expression Data

doi: 10.1155/2014/213656

Figure Lengend Snippet: Summary of ICA software.

Article Snippet: 3 , HiPerSAT , Keith et al. [ ] , C++, MATLAB, and EEGLAB , (i) Integration of FastICA, Informax, and SOBI algorithms (ii) Data whitening is provided.

Techniques: Software, Extraction

Journal: Computational Intelligence and Neuroscience

Article Title: PWC-ICA: A Method for Stationary Ordered Blind Source Separation with Application to EEG

doi: 10.1155/2016/9754813

Figure Lengend Snippet: Comparison of Amari indices for different BSS approaches trained on randomly mixed VAR simulated data. The algorithms with average Amari indices significantly lower ( p < 0.05) than AMICA, Extended Infomax, and FastICA are bold, with the largest p value for the three comparisons shown in parentheses. The cells that are not bold had Amari index distributions that were not significantly lower ( p < 0.05) than at least one of the algorithms AMICA, Extended Infomax, and FastICA. The average baseline was computed by generating 50,000 random demixing matrices for each of the 20 mixing matrices, resulting in a distribution of one million Amari indices, the average of which is reported.

Article Snippet: More importantly, FastICA, Infomax ICA, AMICA, and the proposed Hilbert approach find many components with good dipole RV values but that do not correlate well with a source and/or are not physically close to a source they correlate well with.

Techniques: Comparison

Journal: Computational Intelligence and Neuroscience

Article Title: PWC-ICA: A Method for Stationary Ordered Blind Source Separation with Application to EEG

doi: 10.1155/2016/9754813

Figure Lengend Snippet: Average number of sources that are matched by a demixing solution component with absolute correlation ≥ 0.7 per experiment. Recall that Experiments 1 and 3 had 10 sources and Experiment 2 was generated by 5 sources. Bold values correspond to the significant PWC-ICA algorithms (by the Amari index) as described in Table 1 .

Article Snippet: More importantly, FastICA, Infomax ICA, AMICA, and the proposed Hilbert approach find many components with good dipole RV values but that do not correlate well with a source and/or are not physically close to a source they correlate well with.

Techniques: Generated

Journal: PLoS ONE

Article Title: Independent Component Analysis for Brain fMRI Does Indeed Select for Maximal Independence

doi: 10.1371/journal.pone.0073309

Figure Lengend Snippet: A Gaussian has zero excess kurtosis. Here as in Example 2 of the original paper . The four vertical lines at correspond to the relative sizes of the small box, the medium box, the large box, and a very large box corresponding to the maximal kurtosis case. Note that the medium and large box experiments have near zero excess kurtosis, i.e., kurtosis value matching that of a Gaussian . In addition, the pdfs of these sources are bimodal (see inset figures), ensuring that ICA algorithms designed for unimodal super-Gaussian distributions such as Infomax and FastICA with standard parameter settings, will likely fail. At the bottom of the figure are the ISI values (see Equation (2)) for the various algorithms at those four points (see for full list). Also note the best separation performance of Infomax and FastICA for the maximum kurtosis case, which corresponds to almost the lowest level of sparsity.

Article Snippet: Summary : FastICA, Infomax (super), and ICA-EBM perform well , , , , .

Techniques:

Journal: PLoS ONE

Article Title: Independent Component Analysis for Brain fMRI Does Indeed Select for Maximal Independence

doi: 10.1371/journal.pone.0073309

Figure Lengend Snippet: Source estimates for the four cases indicated in Figure 1 .

Article Snippet: Summary : FastICA, Infomax (super), and ICA-EBM perform well , , , , .

Techniques:

Journal: PLoS ONE

Article Title: Independent Component Analysis for Brain fMRI Does Indeed Select for Maximal Independence

doi: 10.1371/journal.pone.0073309

Figure Lengend Snippet: Tabulated results for the so-called [8] ICA “promotional material.”

Article Snippet: Summary : FastICA, Infomax (super), and ICA-EBM perform well , , , , .

Techniques:

Journal: Cell reports

Article Title: Dentate spikes and external control of hippocampal function

doi: 10.1016/j.celrep.2021.109497

Figure Lengend Snippet: KEY RESOURCES TABLE

Article Snippet: Next, independent component analysis (ICA) was applied to the filtered LFP data using the FastICA MATLAB toolbox ( ) by specifying the number of principal components that were obtained in the previous steps for both PCA-based dimensionality reduction and the target number of resulting independent components.

Techniques: Software