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: 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