mne-tools · Aniketsy · Mar 15, 2026 · Mar 15, 2026 · Mar 15, 2026 · Mar 21, 2026
@@ -0,0 +1 @@
+Enable sphinx-tags and add tags to docs, tutorials, and examples. by `Aniket Singh Yadav`_.
@@ -116,6 +116,7 @@
     "sphinx_copybutton",
     "sphinx_design",
     "sphinx_gallery.gen_gallery",
+    "sphinx_tags",
     "sphinxcontrib.bibtex",
     "sphinxcontrib.youtube",
     "sphinxcontrib.towncrier.ext",
@@ -169,6 +170,14 @@
 
 towncrier_draft_working_directory = str(curpath.parent)
 
+# -- sphinx-tags configuration ------------------------------------------------
+
+tags_create_tags = True
+tags_output_dir = "_tags"
+tags_page_title = "Tag"
+tags_overview_title = "All Tags"
+tags_extension = ["rst"]
+
 # -- Intersphinx configuration -----------------------------------------------
 
 intersphinx_mapping = {

@@ -3,6 +3,8 @@
 Contributing guide
 ==================
 
+.. tags:: development, contributing, governance
+
 .. highlight:: console
 
 Thanks for taking the time to contribute! MNE-Python is an open-source project

@@ -1,6 +1,8 @@
 MNE-Python Development
 ======================
 
+.. tags:: development, contributing, governance
+
 .. NOTE: this first section (up until "overview of contribution process") is
    basically a copy/paste of CONTRIBUTING.md from the repository root, with one
    sentence deleted to avoid self-referential linking. Changes made here should

@@ -1,6 +1,8 @@
 Roadmap
 =======
 
+.. tags:: development, roadmap, planning
+
 This page describes some of the major medium- to long-term goals for
 MNE-Python. These are goals that require substantial effort and/or
 API design considerations. Some of these may be suitable for Google Summer of

@@ -3,6 +3,8 @@
 What's new
 ==========
 
+.. tags:: development, changelog, releases
+
 Changes for each version of MNE-Python are listed below.
 
 .. toctree::

@@ -3,6 +3,8 @@
 How to cite MNE-Python
 ======================
 
+.. tags:: documentation, citation, publication
+
 Citing the software
 -------------------
 

@@ -3,6 +3,8 @@
 Papers citing MNE-Python
 ========================
 
+.. tags:: documentation, citation, publication
+
 Estimates provided by Google Scholar as of 16 December 2024:
 
 - `MNE (2,190) <https://scholar.google.com/scholar?cites=12188330066413208874&as_ylo=2014>`_

@@ -4,6 +4,8 @@
 The typical M/EEG workflow
 ==========================
 
+.. tags:: documentation, workflow, preprocessing, source-localization
+
 Overview
 ========
 

@@ -3,6 +3,8 @@
 Datasets Overview
 #################
 
+.. tags:: documentation, datasets, examples
+
 .. note:: Contributing datasets to MNE-Python
     :class: sidebar
 

@@ -3,6 +3,8 @@
 Design philosophy
 =================
 
+.. tags:: documentation, design, workflow
+
 Interactive versus scripted analysis
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 

@@ -1,6 +1,8 @@
 Glossary
 ========
 
+.. tags:: documentation, glossary, terminology
+
 The Glossary provides short definitions of vocabulary specific to MNE-Python and
 general neuroimaging concepts. If you think a term is missing, please consider
 `creating a new issue`_ or `opening a pull request`_ to add it.

@@ -3,6 +3,8 @@
 Algorithms and other implementation details
 ===========================================
 
+.. tags:: documentation, implementation, internals
+
 This page describes some of the technical details of MNE-Python implementation.
 
 .. _units:

@@ -3,6 +3,8 @@
 Documentation overview
 ======================
 
+.. tags:: documentation, tutorials, examples, api-reference
+
 .. note::
 
    If you haven't already installed MNE-Python, please take a look

@@ -4,6 +4,8 @@
 Frequently Asked Questions (FAQ)
 ================================
 
+.. tags:: help, faq, troubleshooting
+
 .. highlight:: python
 
 General MNE-Python issues

@@ -3,6 +3,8 @@
 Getting help
 ^^^^^^^^^^^^
 
+.. tags:: help, faq, troubleshooting, community
+
 There are several places to obtain help with MNE software tools.
 
 - The `MNE Forum`_ is a good placed to go for both troubleshooting and general

@@ -3,6 +3,8 @@
 Getting started with Python
 ===========================
 
+.. tags:: help, beginners, python, learning
+
 `Python`_ is a modern general-purpose object-oriented high-level programming
 language. There are many general introductions to Python online; here are a
 few:

@@ -3,6 +3,8 @@
 Migrating from other analysis software
 ======================================
 
+.. tags:: help, migrating, interoperability
+
 Here we offer some tips on how to migrate from other analysis software.
 
 EEGLAB

@@ -39,3 +39,4 @@ MNE-Python Homepage
    API Reference <api/python_reference>
    Get Help <help/index>
    Development <development/index>
+   Tags <_tags/tagsindex>
@@ -3,6 +3,8 @@
 Advanced setup
 ==============
 
+.. tags:: installation, advanced, jupyter, troubleshooting
+
 Working with Jupyter Notebooks and JupyterLab
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 

@@ -4,6 +4,8 @@
 Testing your installation
 =========================
 
+.. tags:: installation, setup, troubleshooting
+
 To make sure MNE-Python was installed correctly, type the following command in
 a terminal::
 

@@ -1,6 +1,8 @@
 Installing FreeSurfer
 =====================
 
+.. tags:: installation, freesurfer, source-localization, bem
+
 `FreeSurfer <fs-wiki_>`_ is software for analysis and visualization of MRI data.
 In the MNE ecosystem, freesurfer is used to convert structural MRI scans into
 models of the scalp, inner/outer skull, and cortical surfaces, which are used

@@ -3,6 +3,8 @@
 IDE integration (VSCode, Spyder, etc.)
 ======================================
 
+.. tags:: installation, setup, ide, vscode
+
 Most users find it convenient to write and run their code in an `Integrated
 Development Environment`_ (IDE). Some popular choices for scientific
 Python development are:

@@ -6,6 +6,8 @@
 Installing MNE-Python
 ======================
 
+.. tags:: installation, setup, beginners, advanced
+
 
 .. when https://github.com/executablebooks/sphinx-design/issues/66 is fixed,
    prepend |cloud-arrow-down| |ensp| to the "Download installers" button text

@@ -3,6 +3,8 @@
 MNE-Python installers
 =====================
 
+.. tags:: installation, setup, beginners
+
 MNE-Python installers are the easiest way to install MNE-Python and
 all dependencies. They also provide many additional
 Python packages and tools. Got any questions? Let us know on the `MNE Forum`_!

@@ -4,6 +4,8 @@
 Install via :code:`pip` or :code:`conda`
 ========================================
 
+.. tags:: installation, setup, pip, conda
+
 .. hint::
    If you're unfamiliar with Python, we recommend using our :ref:`installers`
    instead.

@@ -5,6 +5,8 @@
 Installing Python
 =================
 
+.. tags:: installation, setup, python, beginners
+
 MNE-Python requires Python and several Python packages. MNE-Python
 version |version| requires Python version |min_python_version| or higher.
 

@@ -5,6 +5,8 @@
 Installing MNE-C
 ================
 
+.. tags:: installation, mne-c, legacy-tools
+
 System requirements
 ^^^^^^^^^^^^^^^^^^^
 

@@ -1,6 +1,8 @@
 Overview of the MNE tools suite
 ===============================
 
+.. tags:: installation, interoperability, mne-c, mne-python
+
 MNE-Python is an open-source Python module for processing, analysis, and
 visualization of functional neuroimaging data (EEG, MEG, sEEG, ECoG, and
 fNIRS). There are several related or interoperable software packages that you

@@ -1,6 +1,8 @@
 Updating MNE-Python
 ===================
 
+.. tags:: installation, updating, pip, conda
+
 If you want to update MNE-Python to a newer version, there are a few different
 options, depending on how you originally installed it.
 

@@ -5,6 +5,8 @@
 Current Project Leadership and Institutional Partners
 =====================================================
 
+.. tags:: development, governance, community
+
 .. _maintainer-team-people:
 
 Maintainer Team

@@ -5,6 +5,8 @@
 Brainstorm raw (median nerve) dataset
 =====================================
 
+.. tags:: examples, datasets, meg
+
 Here we compute the evoked from raw for the Brainstorm
 tutorial dataset. For comparison, see :footcite:`TadelEtAl2011` and
 https://neuroimage.usc.edu/brainstorm/Tutorials/MedianNerveCtf.

@@ -5,6 +5,8 @@
 HF-SEF dataset
 ==============
 
+.. tags:: examples, datasets, meg, evoked
+
 This example looks at high-frequency SEF responses.
 """
 # Author: Jussi Nurminen (jnu@iki.fi)

@@ -4,6 +4,8 @@
 Kernel OPM phantom data
 =======================
 
+.. tags:: examples, datasets, meg, evoked, inverse
+
 In this dataset, a Neuromag phantom was placed inside the Kernel OPM helmet and
 stimulated with 7 modules active (121 channels). Here we show some example traces.
 """

@@ -5,6 +5,8 @@
 Single trial linear regression analysis with the LIMO dataset
 =============================================================
 
+.. tags:: examples, datasets, eeg, statistics, epochs
+
 Here we explore the structure of the data contained in the
 `LIMO dataset`_.
 This example replicates and extends some of the main analysis

@@ -4,6 +4,8 @@
 Optically pumped magnetometer (OPM) data
 ========================================
 
+.. tags:: examples, datasets, meg, source-localization
+
 In this dataset, electrical median nerve stimulation was delivered to the
 left wrist of the subject. Somatosensory evoked fields were measured using
 nine QuSpin SERF OPMs placed over the right-hand side somatomotor area. Here

@@ -5,6 +5,8 @@
 From raw data to dSPM on SPM Faces dataset
 ==========================================
 
+.. tags:: examples, datasets, preprocessing, ica, inverse, source-localization
+
 Runs a full pipeline using MNE-Python. This example does quite a bit of processing, so
 even on a fast machine it can take several minutes to complete.
 """

@@ -5,6 +5,8 @@
 Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP)
 ===========================================================================
 
+.. tags:: examples, decoding, eeg, csp
+
 Decoding of motor imagery applied to EEG data decomposed using CSP. A
 classifier is then applied to features extracted on CSP-filtered signals.
 

@@ -5,6 +5,8 @@
 Decoding in time-frequency space using Common Spatial Patterns (CSP)
 ====================================================================
 
+.. tags:: examples, decoding, time-frequency, classification
+
 The time-frequency decomposition is estimated by iterating over raw data that
 has been band-passed at different frequencies. This is used to compute a
 covariance matrix over each epoch or a rolling time-window and extract the CSP

@@ -5,6 +5,8 @@
 Representational Similarity Analysis
 ====================================
 
+.. tags:: examples, decoding, statistics, classification
+
 Representational Similarity Analysis is used to perform summary statistics
 on supervised classifications where the number of classes is relatively high.
 It consists in characterizing the structure of the confusion matrix to infer

@@ -5,6 +5,8 @@
 Decoding source space data
 ==========================
 
+.. tags:: examples, decoding, meg, source-localization, classification
+
 Decoding to MEG data in source space on the left cortical surface. Here
 univariate feature selection is employed for speed purposes to confine the
 classification to a small number of potentially relevant features. The

@@ -5,6 +5,8 @@
 Continuous Target Decoding with SPoC
 ====================================
 
+.. tags:: examples, decoding, meg, connectivity, classification
+
 Source Power Comodulation (SPoC) :footcite:`DahneEtAl2014` allows to identify
 the composition of
 orthogonal spatial filters that maximally correlate with a continuous target.

@@ -5,6 +5,8 @@
 Decoding sensor space data with generalization across time and conditions
 =========================================================================
 
+.. tags:: examples, decoding, meg, epochs, classification
+
 This example runs the analysis described in :footcite:`KingDehaene2014`. It
 illustrates how one can fit a linear classifier to identify a discriminatory
 topography at a given time instant and subsequently assess whether this linear

@@ -5,6 +5,8 @@
 Analysis of evoked response using ICA and PCA reduction techniques
 ==================================================================
 
+.. tags:: examples, decoding, ica, evoked, epochs
+
 This example computes PCA and ICA of evoked or epochs data. Then the
 PCA / ICA components, a.k.a. spatial filters, are used to transform
 the channel data to new sources / virtual channels. The output is
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		Enable sphinx-tags and add tags to docs, tutorials, and examples. by `Aniket Singh Yadav`_.