Addressing changes

Author: 1himan

examples/preprocessing/plot_epoch_quality.py

@@ -110,11 +110,15 @@
 # Following visual inspection, bad epochs can be discarded using the
 # :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. 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>`.
+# 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)}")