[Mne_analysis] ValueError: The eigenspectrum of the leadfield at this voxel is complex. Consider reducing the rank of the leadfield by using reduce_rank=True.

[pooja prabhu]

        External Email - Use Caution        

Hai Alex,
Thanks for help.
Here is a snippet :
##creating volume source space
src= setup_volume_source_space(subj, pos=5.0,mri=aseg_fname,
bem=fname_model,mindist=7.0,
subjects_dir=subjects_dir)
##forward model
fwd = mne.make_forward_solution(info=raw.info, trans=None, src=src,
bem=fname_bem,
mindist=2.0, meg=False, eeg=True, n_jobs=1)
##computing noise covariance
noise_cov = mne.compute_covariance(epochs, tmin=0, tmax=0.2,
 method='empirical', rank='full')
##computing data covariance
data_cov = mne.compute_covariance(epochs, tmin=0.2, tmax=epochs.tmax,
method='empirical',
rank='full')
##
 filters = make_lcmv(info=epochs.info, forward=fwd, data_cov=data_cov,
reg=0.05, noise_cov=noise_cov, pick_ori='max-power',
weight_norm='nai',reduce_rank=True)

Let me know anything more is required.
Thank You

On Tue, Sep 3, 2019 at 8:34 AM pooja prabhu <prabhuppooja at gmail.com> wrote:

> Hai Eric,
> I tried exclude=10 but no luck.
> What else i can change to solve this error?
>
> On Sat, Aug 31, 2019 at 3:35 PM pooja prabhu <prabhuppooja at gmail.com>
> wrote:
>
>> Hai group,
>> I encountered the problem while executing the function make_lcmv. The
>> error is ,
>> ValueError: The eigenspectrum of the leadfield at this voxel is complex.
>> Consider reducing the rank of the leadfield by using reduce_rank=True.
>>
>> As recommended i changed included the parameter reduce_rank=True in the
>> function 'make_lcmv', even after that the code gives the same error.
>> I am not sure what is the problem.
>> This work uses 19 channel EEG signals.
>> -------------------------------------
>> Snippet of the code:
>> src= setup_volume_source_space(subj, pos=5.0,mri=aseg_fname,
>> bem=fname_model,mindist=7.0, subjects_dir=subjects_dir)
>> fwd = mne.make_forward_solution(info=raw.info, trans=None, src=src,
>> bem=fname_bem, mindist=2.0, meg=False, eeg=True, n_jobs=1)
>> noise_cov = mne.compute_covariance(epochs, tmin=0, tmax=0.2,
>> method='empirical',
>>                                    rank=None)
>> data_cov = mne.compute_covariance(epochs, tmin=0.2, tmax=epochs.tmax,
>>                                   method='empirical', rank='full')
>> filters = make_lcmv(info=epochs.info, forward=fwd, data_cov=data_cov,
>> reg=0.05,
>>                     noise_cov=noise_cov, pick_ori='max-power',
>>                     weight_norm='nai',reduce_rank=True)
>> ---------------------------------------
>> --
>> Thank You
>> Pooja Prabhu
>>
>>
>
> --
> Thank You
> Pooja Prabhu
>
>

-- 
Thank You
Pooja Prabhu
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