[Mne_analysis] 'shrunk' vs ‘empirical’ for the rest alpha power

[Elena Orekhova]

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Hi,

I try to localize rest alpha band power using sLORETA.



I calculated noise covariance from the empty room data using:



noise_cov = mne.compute_raw_covariance(raw_noise, method=['shrunk',
‘empirical’])



The 'shrunk' was said to be a better choice. However, the result looks
weird with the 'shrunk’ and much more normal with ‘empirical’. (This
subject has prominent occipital alpha peak in sensors.)



The stc power plots for both methods are attached; the only parameter
changed was method=['shrunk'] vs method= [‘empirical’]


Elena



p.s.

The code is below.

I use :



Platform:      Darwin-18.7.0-x86_64-i386-64bit

Python:        3.6.8 |Anaconda, Inc.| (default, Dec 29 2018, 19:04:46)  [GCC
4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]

Executable:    /Users/elena/anaconda3/envs/mne/bin/python

CPU:           i386: 4 cores

Memory:        16.0 GB



mne:           0.20.0

numpy:         1.16.2 {blas=mkl_rt, lapack=mkl_rt}

scipy:         1.2.1

matplotlib:    3.0.3 {backend=Qt5Agg}



sklearn:       0.20.3

numba:         Not found

nibabel:       2.4.0

cupy:          Not found

pandas:        0.24.2

dipy:          0.16.0

mayavi:        4.7.0.dev0 {qt_api=pyqt5, PyQt5=5.9.2}

pyvista:       Not found

vtk:           8.1.2


##########

epo_rest = mne.read_epochs(raw_fname , proj=False, verbose=None)

raw_noise = io.read_raw_fif(raw_fname, preload=True)
raw_noise.filter(2, 40, fir_design='firwin')
noise_cov = mne.compute_raw_covariance(raw_noise, method=['shrunk',
'empirical'])

inverse_operator = make_inverse_operator(epo_rest.info, fwd, noise_cov,
loose=0.2, depth=0.8, verbose=True)

method = 'sLORETA'

lambda2 = 1. / snr ** 2
 bandwidth =  'hann'

stcs_rest = compute_source_psd_epochs(epo_rest[:n_epochs_use],
inverse_operator,
                                 lambda2=lambda2,
                                 method=method, fmin=8, fmax=13,
                                 bandwidth=bandwidth,
                                 return_generator=True, verbose=True)


    psd_avg = 0.
    for i, stc_rest in enumerate(stcs_rest):
    psd_avg += stc_rest.data
    psd_avg /= n_epochs_use
    freqs = stc_rest.times  # the frequencies are stored here
    stc_rest.data = psd_avg


clim=dict(kind='percent', lims=[50, 75, 100] )
kwargs = dict(initial_time=10.0,  clim=clim, hemi='both',
subjects_dir=subjects_dir,time_unit='s',   size=(600, 600))

brain = stc_rest.plot(figure=1, **kwargs)
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