Dear all,
I'm new to PyMVPA, and now trying to apply a Linear SVM to EEG data, in
order to see when the EEG data starts to allow classification of
different conditions. The strength of EEG is temporal resolution, so
something akin to a temporal searchlight seems to be working right now.
I don't expect the spatial information to be so interesting, but I want
to check the sensitivity of features (electrodes) nonetheless. I found
some instructions on how to do so on the FAQ page:
sensitivities = []
def store_me(data, node, result):
... sens = node.measure.get_sensitivity_analyzer(force_train=False)(data)
... sensitivities.append(sens)
cv = CrossValidation(SMLR(), OddEvenPartitioner(), callback=store_me)
merror = cv(dataset)
len(sensitivities)
2
sensitivities[0].shape == (len(dataset.uniquetargets), dataset.nfeatures)
True
I have copied all this into my own script, but it doesn't seem to be
working. That is, the sensitivity is calculated in the store_me callback
function, but it doesn't seem to be stored in the sensitivities list.
Here's my own code:
def store_me(data, node, result):
sens = node.measure.get_sensitivity_analyzer(force_train=False)(data)
sensitivities.append(sens)
sensitivities = []
def sensitivityMVPA():
filenames = ['01-exp_AVGall', '02-exp_AVGall', '03-exp_AVGall',
'04-exp_AVGall', '05-exp_AVGall', '06-exp_AVGall', '07-exp_AVGall',
'08-exp_AVGall', '09_AVGall', '10-exp_AVGall', '11-exp_AVGall', '12_AVGall']
electrodes =
['Fp1','Fz','F3','F7','FC5','FC1','C3','T7','TP9','CP5','CP1','Pz','P3','P7','O1','Oz','O2','P4','P8','TP10','CP6','CP2','C4','T8','FC6','FC2','F4','F8','Fp2','Cz']
epochlength = 200
timepoints = 3100 + arange(0,3000,epochlength)
results = zeros((len(filenames),len(timepoints)))
all_sensitivities = {}
for fileno in range(len(filenames)): # loop through subjects
filename = filenames[fileno]
print filename
BVDS = BrainVisionDataset('../data/%s'%filename)
cuetypes = classifySegments(BVDS)
cuetypes = np.array(cuetypes)
indices = sort(concatenate(( (cuetypes == 1).nonzero()[0][:90] ,
(cuetypes == 8).nonzero()[0][:90] ))) # first 90 trials from conditions
1 and 8
epoch_sensitivities = {}
for epochno in range(len(timepoints)): # loop through 200 ms epochs
sensitivities = [] # clear sensitivities
data = mean(squeeze(BVDS.GetSegmentChannelData(list(indices+1),
electrodes,
baselinesamples=arange(3000,3100)))[:,:,timepoints[epochno]:timepoints[epochno]+epochlength-1],
axis=2)
ds = dataset_wizard(samples=data, targets=cuetypes[indices],
chunks=range(len(indices)))
cvte = CrossValidation(LinearCSVMC(), NFoldPartitioner(),
errorfx=lambda p, t: np.mean(p == t), callback=store_me) # callback
function is called, and sensitivities are calculated
cv_results = cvte(ds)
results[fileno,epochno] = np.mean(cv_results)
epoch_sensitivities[epochno] = sensitivities # here,
sensitivities is empty
all_sensitivities[filename] = epoch_sensitivities
return results, all_sensitivities
Any help would be appreciated!
Best,
Marius
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