[pymvpa] classification based on individual parameter estimates from FSL
d.soto.b at gmail.com
Tue Aug 5 15:40:39 UTC 2014
Hi Michael (and all), just a quick clarification on your previous response
to my query relating classification based on individual parameter estimates
(PEs) - you mentioned I could use the PEs associated with the temporal
derivative or even the PEs associated with a set of basis
functions....however I wonder that this PEs would not be independent (as
would be PEs obtained from different runs)
....would it be okay to use those PEs anyways?
A second related thing is that I have not been using the PEs exactly but
the Contrast of PEs (i.e. COPES in FSL)
associated with each EV- I have 16 EVs (8 per class) and hence obtained
COPES such that
I dont see why it would make any difference to work wit COPEs rather than
PEs, except that only with the later I could boost my dataset by using the
temporal derivatives or basis functions....
On Fri, Jul 4, 2014 at 2:33 PM, Michael Hanke <mih at debian.org> wrote:
> On Tue, Jul 01, 2014 at 12:25:40AM +0100, David Soto wrote:
> > Hi Michael, indeed ..well done for germany today! :).
> > Thanks for the reply and the suggestion on KNN
> > I should have been more clear that for each subject I have the
> > following *block
> > *sequences
> > ababbaabbaabbaba in TASK 1
> > ababbaabbaabbaba in TASK 2
> > this explains that I have 8 a-betas and 8 b-betas for each task
> > AND for each subject..so if i concatenate & normalize all the beta data
> > across subjects I will have 8 x 19 (subjects)= 152 beta images for class
> > and the same for class b
> Ah, I guess you model each task with two regressors (hrf + derivative?).
> You can also use a basis function set and get even more betas...
> > then could I use SVM searchlight trained to discriminate a from b in
> > betas and tested in the task2 betas?
> yes, no problem.
> PS: Off to enjoy the quarter finals ... ;-)
> Michael Hanke
> Pkg-ExpPsy-PyMVPA mailing list
> Pkg-ExpPsy-PyMVPA at lists.alioth.debian.org
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