mne_notebook_1_preproc.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import mne\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fname = \"oddball_example_small-fif.gz\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read in raw data; raw objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw = mne.io.read_raw_fif(fname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MNE is *object oriented*. Objects have corresponding methods. Check which by typing `raw.` and pressing TAB:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    raw.resample\n",
    "    raw.filter\n",
    "    raw.drop_channels\n",
    "    ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can we do further preprocessing?.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw.filter(1, 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aha! By default, MNE does not store raw and epochs objects in memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw = mne.io.read_raw_fif(fname, preload=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw.filter(1, 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspecting raw data ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many eog artefacts. We will use ICA to correct these. For this, we create an ICA object\n",
    "and use its `.fit` method on a filtered copy of the raw data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ica = mne.preprocessing.ICA(n_components=20, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ica.fit(raw.copy().filter(8, 35))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ica.plot_components(outlines=\"skirt\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We store \"bad\" components in the ica object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ica.exclude = [1, 10, 14, 17, 18, 19]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could also use one of the automatic algorithms ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bad_idx, scores = ica.find_bads_eog(raw, 'SO2', threshold=2)\n",
    "print(bad_idx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare raw and corrected data ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ica.apply(raw.copy(), exclude=ica.exclude).plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Epochs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For epoching the data, we need event markers. Usually, these are stored in the `raw` object;\n",
    "in MNE, in a stimulus channel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "events = mne.find_events(raw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`events` is simply an array (time in samples, zero, trigger);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "events"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... which we can visualize:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mne.viz.plot_events(events[:100]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For creating an `mne.Epochs` object, we require, in addition to the `raw` object and the `events` array, a dictionary of the intended condition names and the corresponding trigger numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "event_ids = {\"standard/stimulus\": 200, \"target/stimulus\": 100}\n",
    "\n",
    "epochs = mne.Epochs(raw, events, event_id=event_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(changing to the inline backend now to speed things up.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = ica.apply(epochs, exclude=ica.exclude)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = mne.Epochs(raw, events, event_id=event_ids, preload=True)\n",
    "epochs = ica.apply(epochs, exclude=ica.exclude)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `mne.Epochs` constructor has a number of options, such as time window lengths and rejection thresholds.\n",
    "Investigate them on your own."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Epochs` objects also have various methods, different from `raw` objects - e.g., for baselining."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs.apply_baseline((None, 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... and many more ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To subselect only a sample of epochs, a dict-like access mode is available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs[\"target\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observe how tags selected by forward slashes - \"/\" - work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs[\"stimulus\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How does the epoched activity look like?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs[\"target\"].plot_image(picks=[13]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To ensure we have as many Oddball as Standard trials, we can run ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs.equalize_event_counts(event_ids)\n",
    "epochs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can write the Epochs object to disk so we don't have to repeat the\n",
    "preprocessing later ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs.save(\"oddball-epo.fif\")  # remember, the data has been cleaned of bad ICs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs_for_tfr = mne.Epochs(raw, events, event_id=event_ids,\n",
    "                            tmin=-.5, tmax=1.5, preload=True)  # need longer data segment\n",
    "epochs_for_tfr = ica.apply(epochs_for_tfr, exclude=ica.exclude)\n",
    "epochs_for_tfr.equalize_event_counts(event_ids);  # to speed up things\n",
    "epochs_for_tfr.save(\"oddball-long-epo.fif\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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