Repository: mne-tools/mne-python

Issue:
use bibtex in plot_40_artifact_correction_ica.py
convert references in `tutorials/preprocessing/plot_40_artifact_correction_ica.py` to use footcite / footbibliography



Discussion:
I and @NeuroLaunch  will take over this.

Resolving patch:
diff --git a/tutorials/preprocessing/plot_40_artifact_correction_ica.py b/tutorials/preprocessing/plot_40_artifact_correction_ica.py
--- a/tutorials/preprocessing/plot_40_artifact_correction_ica.py
+++ b/tutorials/preprocessing/plot_40_artifact_correction_ica.py
@@ -88,7 +88,8 @@
 # widespread use; Picard is a newer (2017) algorithm that is expected to
 # converge faster than FastICA and Infomax, and is more robust than other
 # algorithms in cases where the sources are not completely independent, which
-# typically happens with real EEG/MEG data. See [1]_ for more information.
+# typically happens with real EEG/MEG data. See
+# :footcite:`AblinEtAl2018` for more information.
 #
 # The ICA interface in MNE-Python is similar to the interface in
 # `scikit-learn`_: some general parameters are specified when creating an
@@ -185,7 +186,8 @@
 # higher values), making it harder for the algorithm to find an accurate
 # solution. A high-pass filter with 1 Hz cutoff frequency is recommended.
 # However, because filtering is a linear operation, the ICA solution found from
-# the filtered signal can be applied to the unfiltered signal (see [2]_ for
+# the filtered signal can be applied to the unfiltered signal (see
+# :footcite:`WinklerEtAl2015` for
 # more information), so we'll keep a copy of the unfiltered
 # `~mne.io.Raw` object around so we can apply the ICA solution to it
 # later.
@@ -374,7 +376,8 @@
 # a virtual ECG channel, so if you have MEG channels it is usually not
 # necessary to pass a specific channel name.
 # `~mne.preprocessing.ICA.find_bads_ecg` also has two options for its
-# ``method`` parameter: ``'ctps'`` (cross-trial phase statistics [3]_) and
+# ``method`` parameter: ``'ctps'`` (cross-trial phase statistics
+# :footcite:`DammersEtAl2008`) and
 # ``'correlation'`` (Pearson correlation between data and ECG channel).
 
 ica.exclude = []
@@ -443,7 +446,8 @@
 # When dealing with multiple subjects, it is also possible to manually select
 # an IC for exclusion on one subject, and then use that component as a
 # *template* for selecting which ICs to exclude from other subjects' data,
-# using `mne.preprocessing.corrmap` [4]_. The idea behind
+# using `mne.preprocessing.corrmap` :footcite:`CamposViolaEtAl2009`.
+# The idea behind
 # `~mne.preprocessing.corrmap` is that the artifact patterns are similar
 # enough across subjects that corresponding ICs can be identified by
 # correlating the ICs from each ICA solution with a common template, and
@@ -453,7 +457,8 @@
 # within it to use as a template.
 #
 # Since our sample dataset only contains data from one subject, we'll use a
-# different dataset with multiple subjects: the EEGBCI dataset [5]_ [6]_. The
+# different dataset with multiple subjects: the EEGBCI dataset
+# :footcite:`SchalkEtAl2004,GoldbergerEtAl2000`. The
 # dataset has 109 subjects, we'll just download one run (a left/right hand
 # movement task) from each of the first 4 subjects:
 
@@ -568,43 +573,6 @@
 # `~mne.preprocessing.ICA` object containing the template does not need
 # to be in the list of ICAs provided to `~mne.preprocessing.corrmap`.
 #
-#
-# References
-# ^^^^^^^^^^
-#
-# .. [1] Ablin P, Cardoso J, Gramfort A (2018). Faster Independent Component
-#        Analysis by Preconditioning With Hessian Approximations. *IEEE
-#        Transactions on Signal Processing* 66:4040–4049.
-#        https://doi.org/10.1109/TSP.2018.2844203
-#
-# .. [2] Winkler I, Debener S, Müller K-R, Tangermann M (2015). On the
-#        influence of high-pass filtering on ICA-based artifact reduction in
-#        EEG-ERP. Proceedings of EMBC-2015, 4101–4105.
-#        https://doi.org/10.1109/EMBC.2015.7319296
-#
-# .. [3] Dammers J, Schiek M, Boers F, Silex C, Zvyagintsev M, Pietrzyk U,
-#        Mathiak K (2008). Integration of amplitude and phase statistics for
-#        complete artifact removal in independent components of neuromagnetic
-#        recordings. *IEEE Transactions on Biomedical Engineering*
-#        55(10):2353–2362. https://doi.org/10.1109/TBME.2008.926677
-#
-# .. [4] Viola FC, Thorne J, Edmonds B, Schneider T, Eichele T, Debener S
-#        (2009). Semi-automatic identification of independent components
-#        representing EEG artifact. *Clinical Neurophysiology* 120(5):868–877.
-#        https://doi.org/10.1016/j.clinph.2009.01.015
-#
-# .. [5] Schalk G, McFarland DJ, Hinterberger T, Birbaumer N, Wolpaw JR (2004).
-#        BCI2000: A General-Purpose Brain-Computer Interface (BCI) System.
-#        *IEEE Transactions on Biomedical Engineering* 51(6):1034-1043.
-#        https://doi.org/10.1109/TBME.2004.827072
-#
-# .. [6] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG,
-#        Mietus JE, Moody GB, Peng C-K, Stanley HE (2000). PhysioBank,
-#        PhysioToolkit, and PhysioNet: Components of a New Research Resource
-#        for Complex Physiologic Signals. *Circulation* 101(23):e215-e220.
-#        https://doi.org/10.1161/01.CIR.101.23.e215
-#
-#
 # .. LINKS
 #
 # .. _`blind source separation`:
@@ -616,3 +584,8 @@
 # .. _`regular expression`: https://www.regular-expressions.info/
 # .. _`qrs`: https://en.wikipedia.org/wiki/QRS_complex
 # .. _`this EEGLAB tutorial`: https://labeling.ucsd.edu/tutorial/labels
+
+###############################################################################
+# References
+# ----------
+# .. footbibliography::


Test patch: