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. Author manuscript; available in PMC: 2011 Aug 1.
Published in final edited form as: Neuroimage. 2011 May 27;57(3):839–855. doi: 10.1016/j.neuroimage.2011.05.055

Figure 4.

Figure 4

simulation results of comparing separation performance of 3 methods 8 sources for each simulated modality: S1 (left) and S2 (right) are designed on purpose with greatly different final vector length (65536 vs. 2000, implying multimodal data); 100 mixtures are generated for each PSNR, here we display PSNR=6dB. The correlations of mixing coefficients between corresponding sources of each modality are listed in the middle, so do their estimations. See jICA separate sources accurately except the 3rd S2 signal, while mCCA estimates the modal connection accurately except that it can not decompose images quite well. mCCA+jICA combine both advantages and improve the performance remarkably.