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      "source": [
        "\n\n# Reading XDF EEG data\n\nHere we read some sample XDF data. Although we do not analyze it here, this\nrecording is of a short parallel auditory response (pABR) experiment\n:footcite:`PolonenkoMaddox2019` and was provided by the [Maddox Lab](https://www.urmc.rochester.edu/labs/maddox.aspx)_.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      "source": [
        "# Authors: Clemens Brunner <clemens.brunner@gmail.com>\n#          Eric Larson <larson.eric.d@gmail.com>\n#\n# License: BSD-3-Clause\n# Copyright the MNE-Python contributors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import pyxdf\n\nimport mne\nfrom mne.datasets import misc\n\nfname = misc.data_path() / \"xdf\" / \"sub-P001_ses-S004_task-Default_run-001_eeg_a2.xdf\"\nstreams, header = pyxdf.load_xdf(fname)\ndata = streams[0][\"time_series\"].T\nassert data.shape[0] == 5  # four raw EEG plus one stim channel\ndata[:4:2] -= data[1:4:2]  # subtract (rereference) to get two bipolar EEG\ndata = data[::2]  # subselect\ndata[:2] *= 1e-6 / 50 / 2  # uV -> V and preamp gain\nsfreq = float(streams[0][\"info\"][\"nominal_srate\"][0])\ninfo = mne.create_info(3, sfreq, [\"eeg\", \"eeg\", \"stim\"])\nraw = mne.io.RawArray(data, info)\nraw.plot(scalings=dict(eeg=100e-6), duration=1, start=14)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## References\n.. footbibliography::\n\n"
      ]
    }
  ],
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