03-epoching_and_evoked_responses.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating epochs and generating evoked responses (ERP/ERF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pathlib\n",
    "import matplotlib\n",
    "\n",
    "import mne\n",
    "import mne_bids\n",
    "\n",
    "matplotlib.use('Qt5Agg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bids_root = pathlib.Path('out_data/sample_BIDS')\n",
    "\n",
    "bids_path = mne_bids.BIDSPath(subject='01',\n",
    "                              session='01',\n",
    "                              task='audiovisual',\n",
    "                              run='01',\n",
    "                              datatype='meg',\n",
    "                              root=bids_root)\n",
    "\n",
    "raw = mne_bids.read_raw_bids(bids_path)\n",
    "raw.load_data()\n",
    "raw.filter(l_freq=0.1, h_freq=40)\n",
    "events, event_id = mne.events_from_annotations(raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "event_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmin = -0.3\n",
    "tmax = 0.5\n",
    "baseline = (None, 0)\n",
    "\n",
    "epochs = mne.Epochs(raw,\n",
    "                    events=events,\n",
    "                    event_id=event_id,\n",
    "                    tmin=tmin,\n",
    "                    tmax=tmax,\n",
    "                    baseline=baseline,\n",
    "                    preload=True)\n",
    "epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>EXERCISE</b>:\n",
    "     <ul>\n",
    "         <li>Create epochs starting 250 ms before the stimulus onset and ending 800 ms after stimulus onset, and apply baseline correctin with a baseline period ranging from -200 to 0 ms.</li>\n",
    "    </ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting epochs based on experimental conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs['Auditory/Right']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs['Auditory']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs['Left']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs['Visual'].plot_image()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>EXERCISE</b>:\n",
    "     <ul>\n",
    "         <li>Extract all epochs with a \"Right\" condition.</li>\n",
    "    </ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs.save(pathlib.Path('out_data') / 'epochs_epo.fif', \n",
    "            overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating evoked data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "evoked_auditory = epochs['Auditory'].average()\n",
    "evoked_visual = epochs['Visual'].average()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "evoked_auditory.plot(spatial_colors=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "evoked_auditory.plot_topomap(ch_type='mag')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "evoked_auditory.plot_joint(picks='mag')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mne.viz.plot_compare_evokeds([evoked_auditory, evoked_visual], picks='mag')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>EXERCISE</b>:\n",
    "     <ul>\n",
    "         <li>Plot a GFP comparison for the \"Visual/Left\" and \"Visual/Right\" conditions of the EEG data.</li>\n",
    "    </ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving evoked data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mne.write_evokeds(fname=pathlib.Path('out_data') / 'evokeds_ave.fif',\n",
    "                  evoked=[evoked_auditory, evoked_visual])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading evoked data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "evokeds = mne.read_evokeds(fname=pathlib.Path('out_data') / 'evokeds_ave.fif')\n",
    "evokeds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "evokeds[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "evoked = mne.read_evokeds(fname=pathlib.Path('out_data') / 'evokeds_ave.fif',\n",
    "                          condition='0.50 * Visual/Left + 0.50 * Visual/Right')\n",
    "evoked"
   ]
  }
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