[pymvpa] Understanding Classification + HRF

[Anaelia Ovalle]

Hi all,

I am a relatively new pyMVPA user and have 2 questions.

1)

I'm working with 10 chunks of data (190 rows each) with 6 classes +
baseline and am looking to classify them using a linear classifier however
my results are <50%. I think there's something wrong with how I may be
setting this up. Being that this is a rapid-event design, I'm looking to
classify groups of voxel activity to their respective classes. In the
tutorial, I use a zscored and polydetrended dataset and apply:

clf = LinearCSVMC()
cvte = CrossValidation(clf, NFoldPartitioner(),
            errorfx= lambda p,t: np.mean(p==t),enable_ca=['stats'])
cv_results = cvte(df)


*However, *since this is an event based task (where the subject's answer is
placed into a certain class), shouldn't I instead be looking to do an
event-related analysis, shown in : http://www.pymvpa.org/
tutorial_eventrelated.html ? Here, would I first do some response modeling
via fitting a hemodynamic response model? If so, how does this fit into my
pattern classification?


Any clarification regarding these steps would be appreciated.


2)

My final goal after classifying neuronal activity is to then have a map for
each class. Is this the same as doing a sensitivity mapping with the pyMVPA
functions for this?



Thank you,

Anaelia Ovalle
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