{
  "cells": [
    {
      "cell_type": "markdown",
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      "source": [
        "\n\n# Interpolate bad channels for MEG/EEG channels\n\nThis example shows how to interpolate bad MEG/EEG channels\n\n- Using spherical splines from :footcite:`PerrinEtAl1989` for EEG data.\n- Using field interpolation for MEG and EEG data.\n\nIn this example, the bad channels will still be marked as bad.\nOnly the data in those channels is replaced.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# Authors: Denis A. Engemann <denis.engemann@gmail.com>\n#          Mainak Jas <mainak.jas@telecom-paristech.fr>\n#\n# License: BSD-3-Clause\n# Copyright the MNE-Python contributors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import mne\nfrom mne.datasets import sample\n\nprint(__doc__)\n\ndata_path = sample.data_path()\nmeg_path = data_path / \"MEG\" / \"sample\"\nfname = meg_path / \"sample_audvis-ave.fif\"\nevoked = mne.read_evokeds(fname, condition=\"Left Auditory\", baseline=(None, 0))\n\n# plot with bads\nevoked.plot(exclude=[], picks=(\"grad\", \"eeg\"))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Compute interpolation (also works with Raw and Epochs objects)\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "evoked_interp = evoked.copy().interpolate_bads(reset_bads=False)\nevoked_interp.plot(exclude=[], picks=(\"grad\", \"eeg\"))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "You can also use minimum-norm for EEG as well as MEG\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "evoked_interp_mne = evoked.copy().interpolate_bads(\n    reset_bads=False, method=dict(eeg=\"MNE\"), verbose=True\n)\nevoked_interp_mne.plot(exclude=[], picks=(\"grad\", \"eeg\"))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## References\n.. footbibliography::\n\n"
      ]
    }
  ],
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