[pymvpa] design matrix identical across sessions

[Wolfgang Pauli]

Hi,

I am trying to perform an mvpa analysis of an experiment in which I have 16
different trial types and 4 sessions, 4 trial types in each session (run).
Based on the tutorial, I was getting started by using fit_event_hrf_model,
like so:

evds = fit_event_hrf_model(ds, events, time_attr='time_coords',
condition_attr=('onset'), return_model=True)

where ds is an openfmri dataset (get_model_bold_dataset).

I was trying to figure out why I would always get the warning that the
design matrix was singular, and eventually ended up investigating the
design matrix of the model.

I used matplotlib to plot the design matrix, split it up into the four
session, and found that the four parts were IDENTICAL. What could I be
doing wrong? Obviously, it shouldn't be the same, because there are
different trial types in the four sessions, and the trial order is also
randomized.

Furthermore, the design matrix has 26 regressors. I don't quite understand
where that number is coming from, as I have 16 unique event types, and 4
sessions.

Best,

Wolfgang
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