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

[Alexandre Gramfort]

hi Junpeng,

you need to define events visually/manually in mne_browse_raw

HTH
A

On Wed, Mar 19, 2014 at 12:47 PM, junpeng.zhang <junpeng.zhang at gmail.com> wrote:
> Hi Alex,
>
> Thank you for kind response.
> I still have a question. Please see the highlighted fonts.
>
>
>> 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.
>
> so you have 3 time series per location. Which is not make stc contain.
>
>> 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?
>
> if you use instances of SourceEstimates (stc objects) you can use labels.
> I assume you use a surface source space. You can look at the code
> of the in_label method.
>
> http://martinos.org/mne/stable/generated/mne.SourceEstimate.html#mne.SourceEstimate.in_label
>
>> 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?
>
> you can get an Evoked instance by average a single Epoch.
> I ever tried to create a instance but in the epilepy raw data,  there is no
> any events  writed in the file.
> When I import  a .eve file, the error will be no event to average...
> How to transform a  raw.fif into a  ave.fif when the raw file has no events
> indicated?
> My epilepsy raw file info: 11s ----702s.
>
> Best wishes,
> Junpeng Zhang
>
>
>> 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)
>
> it does not make sense to you mean_flip unless you have fixed orient
> source space
> or used pick_ori='normal'. You can just average you have positive
> values in the stc.
>
> Hope this helps,
>
> Alex