[Mne_analysis] mne_compute_raw_inverse gave perfectly correlated source current estimates

[Alexandre Gramfort]

hi,

did you check that your labels are correct?

Alex

On Thu, Feb 23, 2012 at 2:02 PM, Kai Hwang <kai.hwang at gmail.com> wrote:
> Hi Alex, I changed --nave to 100 and still got perfect correlations.
> Actually source estimates look identical between --nave 1 and --nave
> 100.
> Any other suggestions?
> Thanks
> Kai
>
> On Thu, Feb 23, 2012 at 2:44 AM, Alexandre Gramfort
> <gramfort at nmr.mgh.harvard.edu> wrote:
>> Hello Kai,
>>
>> a possible explanation is the "nave" used to set the regularization of
>> the inverse problem.
>>
>> if you work with raw data, nave is 1 while with 100 epochs averaged
>> it's 100 and the inverse
>> solution is then far less regularized. A solution is to set the --nave
>> option to a higher value in
>> mne_compute_raw_inverse but you might amplify noise.
>>
>> hope this helps
>>
>> Alex
>>
>> On Thu, Feb 23, 2012 at 3:02 AM, Kai Hwang <kai.hwang at gmail.com> wrote:
>>> Hello,
>>>
>>> I am trying to use mne_compute_raw_inverse to project single trial
>>> epochs onto the surface. The command finishes without erros, but is
>>> generating perfectly correlated source estimates from distant regions.
>>> The scale and sign of current estimates differ, but the correlation is
>>> either 1 or -1.
>>> Below is the command:
>>>
>>> #do forward solution
>>> mne_do_forward_solution --overwrite \
>>> --meas ${s}_prep_ave.fif --subject ${s} --fwd ${s}_prep_ave_fwd.fif --meg --eeg
>>>
>>> #create inverse operator
>>> mne_do_inverse_operator --fwd ${s}_prep_ave_fwd.fif --depth \
>>> --loose 0.2 --meg --eeg --senscov ${s}_prep_cov.fif --subject ${s}
>>>
>>> #project raw data, for each label
>>> mne_compute_raw_inverse --in ${s}_${rn}_sss_ds_raw.fif \
>>> --inv ${s}_prep_ave_fwd.fif-meg-eeg-inv.fif \
>>> --picknormalcomp \
>>> --align_z \
>>> --labeldir ./ALL-Labels \
>>> --orignames \
>>> --out ${s}-{rn}-label-source
>>>
>>> However, the same inverse operator worked fine with averaged evoked
>>> data, results are reasonable when viewed in mne_analyze.
>>> Any idea what could have gone wrong?
>>>
>>> Thanks!
>>> Kai
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