{
  "nbformat_minor": 0, 
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  "cells": [
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "%matplotlib inline"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }, 
    {
      "source": [
        "\n# Working with ECoG data\n\n\nMNE supports working with more than just MEG and EEG data. Here we show some\nof the functions that can be used to facilitate working with\nelectrocorticography (ECoG) data.\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "# Authors: Eric Larson <larson.eric.d@gmail.com>\n#          Chris Holdgraf <choldgraf@gmail.com>\n#\n# License: BSD (3-clause)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.io import loadmat\nfrom mayavi import mlab\n\nimport mne\nfrom mne.viz import plot_trans, snapshot_brain_montage\n\nprint(__doc__)"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }, 
    {
      "source": [
        "Let's load some ECoG electrode locations and names, and turn them into\na :class:`mne.channels.DigMontage` class.\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "mat = loadmat(mne.datasets.misc.data_path() + '/ecog/sample_ecog.mat')\nch_names = mat['ch_names'].tolist()\nelec = mat['elec']\ndig_ch_pos = dict(zip(ch_names, elec))\nmon = mne.channels.DigMontage(dig_ch_pos=dig_ch_pos)\nprint('Created %s channel positions' % len(ch_names))"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }, 
    {
      "source": [
        "Now that we have our electrode positions in MRI coordinates, we can create\nour measurement info structure.\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "info = mne.create_info(ch_names, 1000., 'ecog', montage=mon)"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }, 
    {
      "source": [
        "We can then plot the locations of our electrodes on our subject's brain.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>These are not real electrodes for this subject, so they\n          do not align to the cortical surface perfectly.</p></div>\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "subjects_dir = mne.datasets.sample.data_path() + '/subjects'\nfig = plot_trans(info, trans=None, subject='sample', subjects_dir=subjects_dir)\nmlab.view(200, 70)"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }, 
    {
      "source": [
        "Sometimes it is useful to make a scatterplot for the current figure view.\nThis is best accomplished with matplotlib. We can capture an image of the\ncurrent mayavi view, along with the xy position of each electrode, with the\n`snapshot_brain_montage` function.\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "# We'll once again plot the surface, then take a snapshot.\nfig = plot_trans(info, trans=None, subject='sample', subjects_dir=subjects_dir)\nmlab.view(200, 70)\nxy, im = snapshot_brain_montage(fig, mon)\n\n# Convert from a dictionary to array to plot\nxy_pts = np.vstack(xy[ch] for ch in info['ch_names'])\n\n# Define an arbitrary \"activity\" pattern for viz\nactivity = np.linspace(100, 200, xy_pts.shape[0])\n\n# This allows us to use matplotlib to create arbitrary 2d scatterplots\n_, ax = plt.subplots(figsize=(10, 10))\nax.imshow(im)\nax.scatter(*xy_pts.T, c=activity, s=200, cmap='coolwarm')\nax.set_axis_off()\nplt.show()"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }
  ], 
  "metadata": {
    "kernelspec": {
      "display_name": "Python 2", 
      "name": "python2", 
      "language": "python"
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    "language_info": {
      "mimetype": "text/x-python", 
      "nbconvert_exporter": "python", 
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      "file_extension": ".py", 
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