[Mne_analysis] how to extract the label time series using MNE-toolbox

[junpeng.zhang]

Hi MNEers, 
Recently I am conducting an epilepy MEG analysis project. 
I used MNE toolbox to create a cortex constrained source space (5891 sources) and calculated the lead fead matrix (free orientaion, 5891 (sources) x 3 (directions xyz) x 306 (channels) matrix ). 
By on house code, I applied  the inverse problem (beamformer) operator to a segment of epileptiform MEG and then got a 5891 (sources) x 3 (directions) x 500 (time points) matrix (SDTM), which is source time series. 

My questions are as, 
1) how to get the information on which source belong to which regions by label operations (for example source A  is included in temporal cortex). If I got such information, I can get a collective time series for a region by averaging (or other ways to get a representative time series) all the source time series in this region. anyone have a quick code  to share it to me?

2) epileptiform MEG is difficult to average. and for such MEG, we have only one "trial".  MNE python is quite suitable to process multiple trials MEG data. How to use the several functions to process single "trials" epileptiform MEG? 
for example, 
if I know SDTM in matlab format, how to use it as the input of the function mne.extract_label_time_course?
The stcs para equals to the SDTM?

The following is the three examples for the functions extracted from 
http://martinos.org/mne/stable/auto_examples/connectivity/plot_mne_inverse_label_connectivity.html#example-connectivity-plot-mne-inverse-label-connectivity-py
stcs = apply_inverse_epochs(epochs, inverse_operator, lambda2, method,
                            pick_ori="normal", return_generator=True)

# Get labels for FreeSurfer 'aparc' cortical parcellation with 34 labels/hemi
labels, label_colors = mne.labels_from_parc('sample', parc='aparc',
                                            subjects_dir=subjects_dir)

# Average the source estimates within each label using sign-flips to reduce
# signal cancellations, also here we return a generator
src = inverse_operator['src']
label_ts = mne.extract_label_time_course(stcs, labels, src, mode='mean_flip',
                                         return_generator=True)
Best wishes, 
Junpeng
2014-03-18



junpeng.zhang
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