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    {
      "execution_count": null, 
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        "%matplotlib inline"
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      "source": [
        "\n# Extracting the time series of activations in a label\n\n\nWe first apply a dSPM inverse operator to get signed activations\nin a label (with positive and negative values) and we then\ncompare different strategies to average the times series\nin a label. We compare a simple average, with an averaging\nusing the dipoles normal (flip mode) and then a PCA,\nalso using a sign flip.\n\n"
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      "source": [
        "# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>\n#\n# License: BSD (3-clause)\n\nimport matplotlib.pyplot as plt\n\nimport mne\nfrom mne.datasets import sample\nfrom mne.minimum_norm import read_inverse_operator, apply_inverse\n\nprint(__doc__)\n\ndata_path = sample.data_path()\nlabel = 'Aud-lh'\nlabel_fname = data_path + '/MEG/sample/labels/%s.label' % label\nfname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'\nfname_evoked = data_path + '/MEG/sample/sample_audvis-ave.fif'\n\nsnr = 3.0\nlambda2 = 1.0 / snr ** 2\nmethod = \"dSPM\"  # use dSPM method (could also be MNE or sLORETA)\n\n# Load data\nevoked = mne.read_evokeds(fname_evoked, condition=0, baseline=(None, 0))\ninverse_operator = read_inverse_operator(fname_inv)\nsrc = inverse_operator['src']\n\n# Compute inverse solution\npick_ori = \"normal\"  # Get signed values to see the effect of sign filp\nstc = apply_inverse(evoked, inverse_operator, lambda2, method,\n                    pick_ori=pick_ori)\n\nlabel = mne.read_label(label_fname)\n\nstc_label = stc.in_label(label)\nmean = stc.extract_label_time_course(label, src, mode='mean')\nmean_flip = stc.extract_label_time_course(label, src, mode='mean_flip')\npca = stc.extract_label_time_course(label, src, mode='pca_flip')\n\nprint(\"Number of vertices : %d\" % len(stc_label.data))\n\n# View source activations\nplt.figure()\nplt.plot(1e3 * stc_label.times, stc_label.data.T, 'k', linewidth=0.5)\nh0, = plt.plot(1e3 * stc_label.times, mean.T, 'r', linewidth=3)\nh1, = plt.plot(1e3 * stc_label.times, mean_flip.T, 'g', linewidth=3)\nh2, = plt.plot(1e3 * stc_label.times, pca.T, 'b', linewidth=3)\nplt.legend([h0, h1, h2], ['mean', 'mean flip', 'PCA flip'])\nplt.xlabel('Time (ms)')\nplt.ylabel('Source amplitude')\nplt.title('Activations in Label : %s' % label)\nplt.show()"
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