diff --git a/doc/documentation.rst b/doc/documentation.rst
index 580e418d9aa..9c0ee7b50e3 100644
--- a/doc/documentation.rst
+++ b/doc/documentation.rst
@@ -395,6 +395,7 @@ this package. You can also find a gallery of these examples in the
     auto_tutorials/plot_source_alignment.rst
     auto_tutorials/plot_forward.rst
     auto_tutorials/plot_compute_covariance.rst
+    auto_tutorials/plot_eeg_no_mri.rst
     auto_tutorials/plot_mne_dspm_source_localization.rst
     auto_tutorials/plot_mne_solutions.rst
     auto_tutorials/plot_dipole_fit.rst
diff --git a/doc/manual/datasets_index.rst b/doc/manual/datasets_index.rst
index 8433a32c39c..3f9900dbfaa 100644
--- a/doc/manual/datasets_index.rst
+++ b/doc/manual/datasets_index.rst
@@ -32,6 +32,10 @@ fsaverage
 For convenience, we provide a function to separately download and extract the
 (or update an existing) fsaverage subject.
 
+.. topic:: Examples
+
+    :ref:`sphx_glr_auto_tutorials_plot_eeg_no_mri.py`
+
 Brainstorm
 ==========
 Dataset fetchers for three Brainstorm tutorials are available. Users must agree to the
diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index ac8ced90a72..8aa1676a521 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -19,6 +19,8 @@ Current
 Changelog
 ~~~~~~~~~
 
+- Add new tutorial on :ref:`sphx_glr_auto_tutorials_plot_eeg_no_mri.py` by `Alex Gramfort`_, and `Joan Massich`_
+
 - Add convenience ``fsaverage`` subject dataset fetcher / updater :func:`mne.datasets.fetch_fsaverage` by `Eric Larson`_
 
 - Add ``fmin`` and ``fmax`` argument to :meth:`mne.time_frequency.AverageTFR.crop` and to :meth:`mne.time_frequency.EpochsTFR.crop` to crop TFR objects along frequency axis by `Dirk Gütlin`_
diff --git a/tutorials/plot_eeg_no_mri.py b/tutorials/plot_eeg_no_mri.py
new file mode 100644
index 00000000000..7125eedfac6
--- /dev/null
+++ b/tutorials/plot_eeg_no_mri.py
@@ -0,0 +1,76 @@
+# -*- coding: utf-8 -*-
+"""
+EEG forward operator with a template MRI
+========================================
+
+This tutorial explains how to compute the forward operator from EEG data
+using the standard template MRI subject ``fsaverage``.
+
+.. important:: Source reconstruction without an individual T1 MRI from the
+               subject will be less accurate. Do not over interpret
+               activity locations which can be off by multiple centimeters.
+
+.. contents:: This tutorial covers:
+   :local:
+   :depth: 2
+
+"""
+
+# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
+#          Joan Massich <mailsik@gmail.com>
+#
+# License: BSD Style.
+
+import os.path as op
+
+import mne
+from mne.datasets import eegbci
+from mne.datasets import fetch_fsaverage
+
+# Download fsaverage files
+fs_dir = fetch_fsaverage(verbose=True)
+subjects_dir = op.dirname(fs_dir)
+
+# The files live in:
+subject = 'fsaverage'
+trans = op.join(fs_dir, 'bem', 'fsaverage-trans.fif')
+src = op.join(fs_dir, 'bem', 'fsaverage-ico-5-src.fif')
+bem = op.join(fs_dir, 'bem', 'fsaverage-5120-5120-5120-bem-sol.fif')
+
+##############################################################################
+# Load the data
+# -------------
+#
+# We use here EEG data from the BCI dataset.
+
+raw_fname, = eegbci.load_data(subject=1, runs=[6])
+raw = mne.io.read_raw_edf(raw_fname, preload=True)
+
+
+# Clean channel names to be able to use a standard 1005 montage
+ch_names = [c.replace('.', '') for c in raw.ch_names]
+raw.rename_channels({old: new for old, new in zip(raw.ch_names, ch_names)})
+
+# Read and set the EEG electrode locations
+montage = mne.channels.read_montage('standard_1005', ch_names=raw.ch_names,
+                                    transform=True)
+
+raw.set_montage(montage)
+raw.set_eeg_reference(projection=True)  # needed for inverse modeling
+
+# Check that the locations of EEG electrodes is correct with respect to MRI
+mne.viz.plot_alignment(
+    raw.info, src=src, eeg=['original', 'projected'], trans=trans, dig=True)
+
+##############################################################################
+# Setup source space and compute forward
+# --------------------------------------
+
+fwd = mne.make_forward_solution(raw.info, trans=trans, src=src,
+                                bem=bem, eeg=True, mindist=5.0, n_jobs=1)
+print(fwd)
+
+# for illustration purposes use fwd to compute the sensitivity map
+eeg_map = mne.sensitivity_map(fwd, ch_type='eeg', mode='fixed')
+eeg_map.plot(time_label='EEG sensitivity', subjects_dir=subjects_dir,
+             clim=dict(lims=[5, 50, 100]))