Let's say I have 3 signals of specific brain areas that are linearly mixed, which I recorded with 3 electrodes plus a reference ground. I used FASTICA to separate the signals and I get 3 new separated signals. However I don't know which signal corresponds to each brain area, since the waveform resemblance with the original measurements is not obvious. I tried calculating the absolute value of the correlation between separated and measured signals to find which combination had a maximum correlation and this way classify the separated signals into their correct measured signal, however this does not always work. Does anyone have a suggestion or know another approach to solving this?
ICA separates source signals by maximizing statistical independence using higher orders of the mean and some cost function. You can sort your ICA components by kurtosis, skewness to match them up. The munkres algorithm is also used to match components.. see: Article An Evaluation of Independent Component Analyses with an Appl...
there is also an implementation on nitrc for masked ica with BOLD data.
Thank you for the suggestion Shady. It seems that the Munkres algorithm/ Hungarian method is good for matching the components of EEG signals after ICA, as I found on this paper where they even mention the EEGLAB function for its implementation:
http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7355373
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