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. Author manuscript; available in PMC: 2015 Apr 15.

Published in final edited form as: Neuroimage. 2014 Jan 2;90:449–468. doi: 10.1016/j.neuroimage.2013.11.046

FIX’s hierarchical classifier. In the data layer, full, feature-selected, temporal, spatial, temporal-feature-selected and spatial-feature-selected datasets ( , , , , and , respectively), are each classified by 5 Classifiers. These Classifiers consist of k-NN, SVM_r (SVM with RBF kernel), SVM_p (SVM with polynomial kernel), SVM_l (linear SVM) and decision tree (simply called tree here). The result is a vector of 30 (5 × 6) probabilities (0 and 1 denoting perfect noise and perfect signal, respectively), which is the input to a fusion-layer classifier, whose output is the probability of IC being signal/noise.

Inline graphic — FIX’s hierarchical classifier. In the data layer, full, feature-selected, temporal, spatial, temporal-feature-selected and spatial-feature-selected datasets ( , , , , and , respectively), are each classified by 5 Classifiers. These Classifiers consist of k-NN, SVM_r (SVM with RBF kernel), SVM_p (SVM with polynomial kernel), SVM_l (linear SVM) and decision tree (simply called tree here). The result is a vector of 30 (5 × 6) probabilities (0 and 1 denoting perfect noise and perfect signal, respectively), which is the input to a fusion-layer classifier, whose output is the probability of IC being signal/noise.