{
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  "cells": [
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "%matplotlib inline"
      ], 
      "outputs": [], 
      "metadata": {
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      }
    }, 
    {
      "source": [
        "\n# Compute sLORETA inverse solution on raw data\n\n\nCompute sLORETA inverse solution on raw dataset restricted\nto a brain label and stores the solution in stc files for\nvisualisation.\n\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "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 apply_inverse_raw, read_inverse_operator\n\nprint(__doc__)\n\ndata_path = sample.data_path()\nfname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'\nfname_raw = data_path + '/MEG/sample/sample_audvis_raw.fif'\nlabel_name = 'Aud-lh'\nfname_label = data_path + '/MEG/sample/labels/%s.label' % label_name\n\nsnr = 1.0  # use smaller SNR for raw data\nlambda2 = 1.0 / snr ** 2\nmethod = \"sLORETA\"  # use sLORETA method (could also be MNE or dSPM)\n\n# Load data\nraw = mne.io.read_raw_fif(fname_raw)\ninverse_operator = read_inverse_operator(fname_inv)\nlabel = mne.read_label(fname_label)\n\nraw.set_eeg_reference()  # set average reference.\nstart, stop = raw.time_as_index([0, 15])  # read the first 15s of data\n\n# Compute inverse solution\nstc = apply_inverse_raw(raw, inverse_operator, lambda2, method, label,\n                        start, stop, pick_ori=None)\n\n# Save result in stc files\nstc.save('mne_%s_raw_inverse_%s' % (method, label_name))"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }, 
    {
      "source": [
        "View activation time-series\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "plt.plot(1e3 * stc.times, stc.data[::100, :].T)\nplt.xlabel('time (ms)')\nplt.ylabel('%s value' % method)\nplt.show()"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }
  ], 
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      "display_name": "Python 2", 
      "name": "python2", 
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      "file_extension": ".py", 
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