{
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    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "%matplotlib inline"
      ], 
      "outputs": [], 
      "metadata": {
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      }
    }, 
    {
      "source": [
        "\n# Re-referencing the EEG signal\n\n\nLoad raw data and apply some EEG referencing schemes.\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "# Authors: Marijn van Vliet <w.m.vanvliet@gmail.com>\n#          Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>\n#\n# License: BSD (3-clause)\n\nimport mne\nfrom mne.datasets import sample\nfrom matplotlib import pyplot as plt\n\nprint(__doc__)\n\n# Setup for reading the raw data\ndata_path = sample.data_path()\nraw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'\nevent_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'\nevent_id, tmin, tmax = 1, -0.2, 0.5\n\n# Read the raw data\nraw = mne.io.read_raw_fif(raw_fname, preload=True)\nevents = mne.read_events(event_fname)\n\n# The EEG channels will be plotted to visualize the difference in referencing\n# schemes.\npicks = mne.pick_types(raw.info, meg=False, eeg=True, eog=True, exclude='bads')"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }, 
    {
      "source": [
        "Apply different EEG referencing schemes and plot the resulting evokeds.\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "reject = dict(eeg=180e-6, eog=150e-6)\nepochs_params = dict(events=events, event_id=event_id, tmin=tmin, tmax=tmax,\n                     picks=picks, reject=reject)\n\nfig, (ax1, ax2, ax3) = plt.subplots(nrows=3, ncols=1, sharex=True)\n\n# No reference. This assumes that the EEG has already been referenced properly.\n# This explicitly prevents MNE from adding a default EEG reference.\nraw.set_eeg_reference([])\nevoked_no_ref = mne.Epochs(raw, **epochs_params).average()\n\nevoked_no_ref.plot(axes=ax1, titles=dict(eeg='EEG Original reference'))\n\n# Average reference. This is normally added by default, but can also be added\n# explicitly.\nraw.set_eeg_reference()\nevoked_car = mne.Epochs(raw, **epochs_params).average()\n\nevoked_car.plot(axes=ax2, titles=dict(eeg='EEG Average reference'))\n\n# Re-reference from an average reference to the mean of channels EEG 001 and\n# EEG 002.\nraw.set_eeg_reference(['EEG 001', 'EEG 002'])\nevoked_custom = mne.Epochs(raw, **epochs_params).average()\n\nevoked_custom.plot(axes=ax3, titles=dict(eeg='EEG Custom reference'))"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }
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