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    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "%matplotlib inline"
      ], 
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      "metadata": {
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      }
    }, 
    {
      "source": [
        "\n# Compute DICS beamfomer on evoked data\n\n\nCompute a Dynamic Imaging of Coherent Sources (DICS) [1]_ beamformer from\nsingle-trial activity in a time-frequency window to estimate source time\ncourses based on evoked data.\n\nReferences\n----------\n.. [1] Gross et al. Dynamic imaging of coherent sources: Studying neural\n       interactions in the human brain. PNAS (2001) vol. 98 (2) pp. 694-699\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "# Author: Roman Goj <roman.goj@gmail.com>\n#\n# License: BSD (3-clause)\n\nimport mne\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom mne.datasets import sample\nfrom mne.time_frequency import csd_epochs\nfrom mne.beamformer import dics\n\nprint(__doc__)\n\ndata_path = sample.data_path()\nraw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'\nevent_fname = data_path + '/MEG/sample/sample_audvis_raw-eve.fif'\nfname_fwd = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'\nlabel_name = 'Aud-lh'\nfname_label = data_path + '/MEG/sample/labels/%s.label' % label_name\nsubjects_dir = data_path + '/subjects'"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }, 
    {
      "source": [
        "Read raw data\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "raw = mne.io.read_raw_fif(raw_fname)\nraw.info['bads'] = ['MEG 2443', 'EEG 053']  # 2 bads channels\n\n# Set picks\npicks = mne.pick_types(raw.info, meg=True, eeg=False, eog=False,\n                       stim=False, exclude='bads')\n\n# Read epochs\nevent_id, tmin, tmax = 1, -0.2, 0.5\nevents = mne.read_events(event_fname)\nepochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,\n                    picks=picks, baseline=(None, 0), preload=True,\n                    reject=dict(grad=4000e-13, mag=4e-12))\nevoked = epochs.average()\n\n# Read forward operator\nforward = mne.read_forward_solution(fname_fwd, surf_ori=True)\n\n# Computing the data and noise cross-spectral density matrices\n# The time-frequency window was chosen on the basis of spectrograms from\n# example time_frequency/plot_time_frequency.py\ndata_csd = csd_epochs(epochs, mode='multitaper', tmin=0.04, tmax=0.15,\n                      fmin=6, fmax=10)\nnoise_csd = csd_epochs(epochs, mode='multitaper', tmin=-0.11, tmax=0.0,\n                       fmin=6, fmax=10)\n\nevoked = epochs.average()\n\n# Compute DICS spatial filter and estimate source time courses on evoked data\nstc = dics(evoked, forward, noise_csd, data_csd, reg=0.05)\n\nplt.figure()\nts_show = -30  # show the 40 largest responses\nplt.plot(1e3 * stc.times,\n         stc.data[np.argsort(stc.data.max(axis=1))[ts_show:]].T)\nplt.xlabel('Time (ms)')\nplt.ylabel('DICS value')\nplt.title('DICS time course of the 30 largest sources.')\nplt.show()\n\n# Plot brain in 3D with PySurfer if available\nbrain = stc.plot(hemi='rh', subjects_dir=subjects_dir,\n                 initial_time=0.1, time_unit='s')\nbrain.show_view('lateral')\n\n# Uncomment to save image\n# brain.save_image('DICS_map.png')"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }
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