[pymvpa] feature sensitivity in MVPA run on EEG data

[J.A. Etzel]

Hmm; this is worryingly sensitive, especially since you have so many 
examples. How many dimensions do you have (I guess channels here, not 
voxels)? Did you average the z-scored data or "raw" data?

I wonder if there is some sort of interaction with time (what order the 
trials were completed in); I know the effects of movement, fatigue, etc. 
are very different with EEG than fMRI, but assume that there is some 
effect. It might help understand the averaging effect if you mix up 
which trials are averaged together (e.g. pick five at random instead of 
consecutive trials). Are there some natural breaks in the data (e.g. 
rest periods or different trial conditions)? If so, perhaps averaging 
within the epochs might produce more sensible results.

Also, what cross-validation scheme are you using? How do you adjust it 
for the number of examples?

good luck,
Jo


On 3/7/2014 1:42 PM, Marius 't Hart wrote:
> Then I also tried averaging across several trials, by taking N
> consecutive trials within the same condition, and discarding the
> remaining trials. The minimum number of trials that were acceptable for
> analysis within the conditions and across subjects was 70 (one subject
> had 104, average close to 90). When I average across 23 trials (so that
> there are a minimum of 3 targets within each condition) the noisiness of
> the data should be minimal, but the Linear SVM performs at 0% for many
> participants across the whole preparation interval and on average at
> slightly above 20%... well below chance! Something must be terribly
> wrong there. When averaging around 5 trials (so that there are 35
> targets or more in each condition) performance looks better. Using sets
> of 10, 15 and 20 trials progressively decreases performance. So it seems
> that somewhere around 5 there is an optimum. I'm not sure how to pick a
> good value here, without trying them all and picking the one that
> performs best.