## Menu |
## ISCTEST: Testing Independent ComponentsThis page contains Matlab code for testing which independent components are statistically significant. The code assumes you have computed ICA for several datasets. In general, you can just split your dataset into two and do ICA separately on those two set. In a neuroimaging context, you perform ICA separately on several subjects or several segments (sessions) of data from a single subject. The code then compares and clusters the independent components for the different datasets (perhaps subjects or sessions) and determines which are found more consistently (i.e. which are more similar across subjects or sessions) than expected by chance. First, you have to choose if you want to test the significance of the consistency based on the mixing matrix or the independent components themselves.
clustering=isctest(Atensor,0.05,0.05,'mixing'); where
clustering=isctest(Stensor,0.05,0.05,'components'); where Note that in both cases you have to do ICA first, this code does not include ICA estimation. (You must used ordinary ICA here, e.g. using the FastICA package. Do not use the ICASSO package which is not compatible with ISCTEST.) The variable [ 5, 3, 4, 0 ]This means that the method found a cluster of consistent components which contains the 5th component of subject #1, the 3rd component of subject #2, the 4th components of subject #3, but no component from subject #4 fitted this particular cluster. Each row should be interpreted separately as one cluster of consistent components. For the theory, see the papers (For version control and historical reference, here is the first version of the method, including testing mixing matrix only as in the first paper above.) |