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      "source": [
        "\n\n# Interpolate EEG data to any montage\n\nThis example demonstrates how to interpolate EEG channels to match a given montage.\nThis can be useful for standardizing\nEEG channel layouts across different datasets (see :footcite:`MellotEtAl2024`).\n\n- Using the field interpolation for EEG data.\n- Using the target montage \"biosemi16\".\n\nIn this example, the data from the original EEG channels will be\ninterpolated onto the positions defined by the \"biosemi16\" montage.\n"
      ]
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        "# Authors: Antoine Collas <contact@antoinecollas.fr>\n# License: BSD-3-Clause\n# Copyright the MNE-Python contributors.\n\nimport matplotlib.pyplot as plt\n\nimport mne\nfrom mne.channels import make_standard_montage\nfrom mne.datasets import sample\n\nprint(__doc__)\nylim = (-10, 10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Load EEG data\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "data_path = sample.data_path()\neeg_file_path = data_path / \"MEG\" / \"sample\" / \"sample_audvis-ave.fif\"\nevoked = mne.read_evokeds(eeg_file_path, condition=\"Left Auditory\", baseline=(None, 0))\n\n# Select only EEG channels\nevoked.pick(\"eeg\")\n\n# Plot the original EEG layout\nevoked.plot(exclude=[], picks=\"eeg\", ylim=dict(eeg=ylim))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Define the target montage\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
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      "outputs": [],
      "source": [
        "standard_montage = make_standard_montage(\"biosemi16\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Use interpolate_to to project EEG data to the standard montage\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
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      "source": [
        "evoked_interpolated_spline = evoked.copy().interpolate_to(\n    standard_montage, method=\"spline\"\n)\n\n# Plot the interpolated EEG layout\nevoked_interpolated_spline.plot(exclude=[], picks=\"eeg\", ylim=dict(eeg=ylim))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Use interpolate_to to project EEG data to the standard montage\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "evoked_interpolated_mne = evoked.copy().interpolate_to(standard_montage, method=\"MNE\")\n\n# Plot the interpolated EEG layout\nevoked_interpolated_mne.plot(exclude=[], picks=\"eeg\", ylim=dict(eeg=ylim))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Comparing before and after interpolation\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "fig, axs = plt.subplots(3, 1, figsize=(8, 6), constrained_layout=True)\nevoked.plot(exclude=[], picks=\"eeg\", axes=axs[0], show=False, ylim=dict(eeg=ylim))\naxs[0].set_title(\"Original EEG Layout\")\nevoked_interpolated_spline.plot(\n    exclude=[], picks=\"eeg\", axes=axs[1], show=False, ylim=dict(eeg=ylim)\n)\naxs[1].set_title(\"Interpolated to Standard 1020 Montage using spline interpolation\")\nevoked_interpolated_mne.plot(\n    exclude=[], picks=\"eeg\", axes=axs[2], show=False, ylim=dict(eeg=ylim)\n)\naxs[2].set_title(\"Interpolated to Standard 1020 Montage using MNE interpolation\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## References\n.. footbibliography::\n\n"
      ]
    }
  ],
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