DOC: Add artifact-cleaning note to epoch quality example. (#13827)

Author: 1himan

doc/conf.py

@@ -180,15 +180,13 @@
     "picard": ("https://mind-inria.github.io/picard/", None),
     "eeglabio": ("https://eeglabio.readthedocs.io/en/latest", None),
     "pybv": ("https://pybv.readthedocs.io/en/latest", None),
-    # should go back below after https://github.com/dipy/dipy/issues/3870 is fixed
-    "dipy": ("https://docs.dipy.org/1.12.0", None),
 }
 intersphinx_mapping.update(
     get_intersphinx_mapping(
         packages=set(
             """
 imageio matplotlib numpy pandas python scipy statsmodels sklearn numba joblib nibabel
-seaborn patsy pyvista nilearn pyqtgraph
+seaborn patsy pyvista dipy nilearn pyqtgraph
 """.strip().split()
         ),
     )

examples/preprocessing/plot_epoch_quality.py

@@ -111,6 +111,14 @@
 # :meth:`mne.Epochs.drop` method. Here, we remove the worst scoring epochs (≥ 0.6)
 # which contained strong artifact activity. The remaining good epochs can then be used
 # for further analysis.
+#
+# .. admonition:: Repairing and retaining epochs with artifacts
+#
+#     In this case, the large frontal deflections are likely blink artifacts, so
+#     dropping is not the only option. For artifacts like these, it can be
+#     preferable to repair the data and keep more epochs, for example with
+#     :ref:`ICA <tut-artifact-ica>` or
+#     :ref:`regression-based artifact correction <tut-artifact-regression>`.
 epochs.drop(np.where(scores >= 0.6)[0])
 print(f"Epochs remaining after dropping scores ≥ 0.6: {len(epochs)}")