[med-svn] [python-mne] 313/353: ENH : add example of LCMV on volume grid

[Yaroslav Halchenko]

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yoh pushed a commit to tag 0.4
in repository python-mne.

commit cb426411559875d909f05e2b63a583cbfb03e6d1
Author: Alexandre Gramfort <alexandre.gramfort at inria.fr>
Date:   Wed Jul 25 11:48:01 2012 +0200

    ENH : add example of LCMV on volume grid
---
 examples/inverse/plot_lcmv_beamformer_volume.py | 66 +++++++++++++++++++++++++
 1 file changed, 66 insertions(+)

diff --git a/examples/inverse/plot_lcmv_beamformer_volume.py b/examples/inverse/plot_lcmv_beamformer_volume.py
new file mode 100644
index 0000000..a7cba57
--- /dev/null
+++ b/examples/inverse/plot_lcmv_beamformer_volume.py
@@ -0,0 +1,66 @@
+"""
+===================================================================
+Compute LCMV inverse solution on evoked data in volume source space
+===================================================================
+
+Compute LCMV inverse solution on an auditory evoked dataset in a volume source
+space. It stores the solution in a nifti file for visualisation e.g. with
+Freeview.
+
+"""
+
+# Author: Alexandre Gramfort <gramfort at nmr.mgh.harvard.edu>
+#
+# License: BSD (3-clause)
+
+print __doc__
+
+import mne
+from mne.datasets import sample
+from mne.fiff import Raw, pick_types
+from mne.beamformer import lcmv
+
+
+data_path = sample.data_path('..')
+raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
+event_fname = data_path + '/MEG/sample/sample_audvis_raw-eve.fif'
+fname_fwd = data_path + '/MEG/sample/sample_audvis-meg-vol-7-fwd.fif'
+fname_cov = data_path + '/MEG/sample/sample_audvis-cov.fif'
+
+###############################################################################
+# Get epochs
+event_id, tmin, tmax = 1, -0.2, 0.5
+
+# Setup for reading the raw data
+raw = Raw(raw_fname)
+raw.info['bads'] = ['MEG 2443', 'EEG 053']  # 2 bads channels
+events = mne.read_events(event_fname)
+
+# Set up pick list: EEG + MEG - bad channels (modify to your needs)
+left_temporal_channels = mne.read_selection('Left-temporal')
+picks = pick_types(raw.info, meg=True, eeg=False, stim=True, eog=True,
+                   exclude=raw.info['bads'], selection=left_temporal_channels)
+
+# Read epochs
+epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
+                    picks=picks, baseline=(None, 0), preload=True,
+                    reject=dict(grad=4000e-13, mag=4e-12, eog=150e-6))
+evoked = epochs.average()
+
+forward = mne.read_forward_solution(fname_fwd)
+
+noise_cov = mne.read_cov(fname_cov)
+noise_cov = mne.cov.regularize(noise_cov, evoked.info,
+                               mag=0.05, grad=0.05, eeg=0.1, proj=True)
+
+data_cov = mne.compute_covariance(epochs, tmin=0.04, tmax=0.15)
+stc = lcmv(evoked, forward, noise_cov, data_cov, reg=0.01)
+
+# Save result in stc files
+stc.save('lcmv-vol')
+
+stc.crop(0.0, 0.2)
+
+# Save result in a 4D nifti file
+img = mne.save_stc_as_volume('lcmv_inverse.nii.gz', stc,
+        forward['src'], mri_resolution=True)  # True for full MRI resolution

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