# Read noise covariance matrix
cov = mne.read_cov(cov_fname)
# Handling average file
setno = 0
evoked = fiff.read_evoked(ave_fname, setno=setno, baseline=(None, 0))
evoked.crop(tmin=0, tmax=0.3)
# Handling forward solution
forward = mne.read_forward_solution(fwd_fname, surf_ori=True)
cov = mne.cov.regularize(cov, evoked.info)
import pylab as pl
pl.figure()
ylim = dict(eeg=[-10, 10], grad=[-400, 400], mag=[-600, 600])
plot_evoked(evoked, ylim=ylim, proj=True)
###############################################################################
# Run solver
alpha = 70 # regularization parameter between 0 and 100 (100 is high)
loose, depth = 0.2, 0.9 # loose orientation & depth weighting
# Compute dSPM solution to be used as weights in MxNE
inverse_operator = make_inverse_operator(evoked.info, forward, cov,
loose=None, depth=depth, fixed=True)
stc_dspm = apply_inverse(evoked, inverse_operator, lambda2=1. / 9.,
method='dSPM')
# Compute MxNE inverse solution
stc, residual = mixed_norm(evoked, forward, cov, alpha, loose=loose,
depth=depth, maxit=3000, tol=1e-4, active_set_size=10,
times = np.linspace(tmin, tmin + n_samples * tstep, n_samples)
# Generate times series from 2 Morlet wavelets
stc_data = np.zeros((len(labels), len(times)))
Ws = morlet(sfreq, [3, 10], n_cycles=[1, 1.5])
stc_data[0][:len(Ws[0])] = np.real(Ws[0])
stc_data[1][:len(Ws[1])] = np.real(Ws[1])
stc_data *= 100 * 1e-9 # use nAm as unit
# time translation
stc_data[1] = np.roll(stc_data[1], 80)
stc = generate_sparse_stc(fwd['src'], labels, stc_data, tmin, tstep,
random_state=0)
###############################################################################
# Generate noisy evoked data
picks = mne.fiff.pick_types(raw.info, meg=True, exclude='bads')
iir_filter = iir_filter_raw(raw, order=5, picks=picks, tmin=60, tmax=180)
evoked = generate_evoked(fwd, stc, evoked_template, cov, snr,
tmin=0.0, tmax=0.2, iir_filter=iir_filter)
###############################################################################
# Plot
plot_sparse_source_estimates(fwd['src'], stc, bgcolor=(1, 1, 1),
opacity=0.5, high_resolution=True)
plt.figure()
plt.psd(evoked.data[0])
plot_evoked(evoked)
"""
==================================
Reading and writing an evoked file
==================================
"""
# Author: Alexandre Gramfort <*****@*****.**>
#
# License: BSD (3-clause)
print __doc__
from mne import fiff
from mne.datasets import sample
from mne.viz import plot_evoked
data_path = sample.data_path('.')
fname = data_path + '/MEG/sample/sample_audvis-ave.fif'
# Reading
evoked = fiff.Evoked(fname, setno=0, baseline=(None, 0))
###############################################################################
# Show result
picks = fiff.pick_types(evoked.info, meg=True, eeg=True,
exclude=evoked.info['bads']) # Pick channels to view
plot_evoked(evoked, picks=picks)
""" ================================== Reading and writing an evoked file ================================== """ # Author: Alexandre Gramfort <*****@*****.**> # # License: BSD (3-clause) print __doc__ from mne import fiff from mne.datasets import sample from mne.viz import plot_evoked data_path = sample.data_path() fname = data_path + "/MEG/sample/sample_audvis-ave.fif" # Reading evoked = fiff.Evoked(fname, setno="Left Auditory", baseline=(None, 0), proj=True) ############################################################################### # Show result: # By using exclude=[] bad channels are not excluded and are shown in red plot_evoked(evoked, exclude=[])
"""
==================================
Reading and writing an evoked file
==================================
"""
# Author: Alexandre Gramfort <*****@*****.**>
#
# License: BSD (3-clause)
print __doc__
from mne import fiff
from mne.datasets import sample
from mne.viz import plot_evoked
data_path = sample.data_path()
fname = data_path + '/MEG/sample/sample_audvis-ave.fif'
# Reading
evoked = fiff.Evoked(fname,
setno='Left Auditory',
baseline=(None, 0),
proj=True)
###############################################################################
# Show result:
# By using exclude=[] bad channels are not excluded and are shown in red
plot_evoked(evoked, exclude=[])
# Read noise covariance matrix
cov = mne.read_cov(cov_fname)
# Handling average file
setno = 0
evoked = fiff.read_evoked(ave_fname, setno=setno, baseline=(None, 0))
evoked.crop(tmin=0, tmax=0.3)
# Handling forward solution
forward = mne.read_forward_solution(fwd_fname, force_fixed=True, surf_ori=True)
cov = mne.cov.regularize(cov, evoked.info)
import pylab as pl
pl.figure()
ylim = dict(eeg=[-10, 10], grad=[-400, 400], mag=[-600, 600])
plot_evoked(evoked, ylim=ylim, proj=True)
###############################################################################
# Run solver
alpha = 70 # regularization parameter between 0 and 100 (100 is high)
loose, depth = 0.2, 0.9 # loose orientation & depth weighting
# Compute dSPM solution to be used as weights in MxNE
inverse_operator = make_inverse_operator(evoked.info,
forward,
cov,
loose=loose,
depth=depth)
stc_dspm = apply_inverse(evoked,
inverse_operator,
lambda2=1. / 9.,
loose=loose, depth=depth, maxit=200, tol=1e-4,
weights=stc_dspm, weights_min=8., debias=True,
wsize=16, tstep=4, window=0.05, return_residual=True)
evoked = mne.fiff.pick_types_evoked(evoked, meg='grad')
residual = mne.fiff.pick_types_evoked(residual, meg='grad')
# Crop to remove edges
stc.crop(tmin=-0.05, tmax=0.3)
evoked.crop(tmin=-0.05, tmax=0.3)
residual.crop(tmin=-0.05, tmax=0.3)
import pylab as pl
pl.figure()
ylim = dict(eeg=[-10, 10], grad=[-200, 250], mag=[-600, 600])
plot_evoked(evoked, ylim=ylim, proj=True,
titles=dict(grad='Evoked Response (grad)'))
pl.figure()
plot_evoked(residual, ylim=ylim, proj=True,
titles=dict(grad='Residual (grad)'))
###############################################################################
# View in 2D and 3D ("glass" brain like 3D plot)
plot_sparse_source_estimates(forward['src'], stc, bgcolor=(1, 1, 1),
opacity=0.1, fig_name="TF-MxNE (cond %s)" % setno,
fig_number=setno, modes=['sphere'],
scale_factors=[1.])
time_label = 'TF-MxNE time=%0.2f ms'
brain = stc.plot('sample', 'inflated', 'rh', fmin=10e-9, fmid=15e-9,
fmax=20e-9, time_label=time_label, smoothing_steps=5,
"""
==================================
Reading and writing an evoked file
==================================
"""
# Author: Alexandre Gramfort <*****@*****.**>
#
# License: BSD (3-clause)
print __doc__
from mne import fiff
from mne.datasets import sample
from mne.viz import plot_evoked
data_path = sample.data_path('.')
fname = data_path + '/MEG/sample/sample_audvis-ave.fif'
# Reading
evoked = fiff.Evoked(fname, setno=0, baseline=(None, 0), proj=True)
###############################################################################
# Show result
picks = fiff.pick_types(evoked.info,
meg=True,
eeg=True,
exclude=evoked.info['bads']) # Pick channels to view
plot_evoked(evoked, picks=picks)