{
  "cells": [
    {
      "cell_type": "markdown",
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      "source": [
        "\n\n# From raw data to dSPM on SPM Faces dataset\n\nRuns a full pipeline using MNE-Python. This example does quite a bit of processing, so\neven on a fast machine it can take several minutes to complete.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>\n#          Denis Engemann <denis.engemann@gmail.com>\n#\n# License: BSD-3-Clause\n# Copyright the MNE-Python contributors.\n\nimport mne\nfrom mne import combine_evoked, io\nfrom mne.datasets import spm_face\nfrom mne.minimum_norm import apply_inverse, make_inverse_operator\nfrom mne.preprocessing import ICA, create_eog_epochs\n\nprint(__doc__)\n\ndata_path = spm_face.data_path()\nsubjects_dir = data_path / \"subjects\"\nspm_path = data_path / \"MEG\" / \"spm\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Load data, filter it, and fit ICA.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "raw_fname = spm_path / \"SPM_CTF_MEG_example_faces1_3D.ds\"\nraw = io.read_raw_ctf(raw_fname, preload=True)  # Take first run\n# Here to save memory and time we'll downsample heavily -- this is not\n# advised for real data as it can effectively jitter events!\nraw.resample(100)\nraw.filter(1.0, None)  # high-pass\nreject = dict(mag=5e-12)\nica = ICA(n_components=0.95, max_iter=\"auto\", random_state=0)\nica.fit(raw, reject=reject)\n# compute correlation scores, get bad indices sorted by score\neog_epochs = create_eog_epochs(raw, ch_name=\"MRT31-2908\", reject=reject)\neog_inds, eog_scores = ica.find_bads_eog(eog_epochs, ch_name=\"MRT31-2908\")\nica.plot_scores(eog_scores, eog_inds)  # see scores the selection is based on\nica.plot_components(eog_inds)  # view topographic sensitivity of components\nica.exclude += eog_inds[:1]  # we saw the 2nd ECG component looked too dipolar\nica.plot_overlay(eog_epochs.average())  # inspect artifact removal"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Epoch data and apply ICA.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "events = mne.find_events(raw, stim_channel=\"UPPT001\")\nevent_ids = {\"faces\": 1, \"scrambled\": 2}\ntmin, tmax = -0.2, 0.6\nepochs = mne.Epochs(\n    raw,\n    events,\n    event_ids,\n    tmin,\n    tmax,\n    picks=\"meg\",\n    baseline=None,\n    preload=True,\n    reject=reject,\n)\ndel raw\nica.apply(epochs)  # clean data, default in place\nevoked = [epochs[k].average() for k in event_ids]\ncontrast = combine_evoked(evoked, weights=[-1, 1])  # Faces - scrambled\nevoked.append(contrast)\nfor e in evoked:\n    e.plot(ylim=dict(mag=[-400, 400]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Estimate noise covariance and look at the whitened evoked data\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "noise_cov = mne.compute_covariance(epochs, tmax=0, method=\"shrunk\", rank=None)\nevoked[0].plot_white(noise_cov)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Compute forward model\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "trans_fname = spm_path / \"SPM_CTF_MEG_example_faces1_3D_raw-trans.fif\"\nsrc = subjects_dir / \"spm\" / \"bem\" / \"spm-oct-6-src.fif\"\nbem = subjects_dir / \"spm\" / \"bem\" / \"spm-5120-5120-5120-bem-sol.fif\"\nforward = mne.make_forward_solution(contrast.info, trans_fname, src, bem)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Compute inverse solution and plot\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "snr = 3.0\nlambda2 = 1.0 / snr**2\ninverse_operator = make_inverse_operator(contrast.info, forward, noise_cov)\nstc = apply_inverse(contrast, inverse_operator, lambda2, method=\"dSPM\", pick_ori=None)\nbrain = stc.plot(\n    hemi=\"both\",\n    subjects_dir=subjects_dir,\n    initial_time=0.170,\n    views=[\"ven\"],\n    clim={\"kind\": \"value\", \"lims\": [3.0, 6.0, 9.0]},\n)"
      ]
    }
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