Minor edit

Author: tsbinns

examples/preprocessing/plot_epoch_quality.py

@@ -105,7 +105,7 @@
 )
 
 # %%
-# Dropping suspicious epochs
+# Handling suspicious epochs
 # --------------------------
 # Following visual inspection, bad epochs can be discarded using the
 # :meth:`mne.Epochs.drop` method. Here, we remove the worst scoring epochs (≥ 0.6)
@@ -114,11 +114,10 @@
 #
 # .. 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>`.
+#     Dropping epochs is not the only option for handling artifacts. It may also be
+#     possible to repair the data and retain these epochs. In this case, the large
+#     deflections are likely blink artifacts, which can be repaired with techniques such
+#     as :ref:`ICA <tut-artifact-ica>` and :ref:`regression <tut-artifact-regression>`.
 epochs.drop(np.where(scores >= 0.6)[0])
 print(f"Epochs remaining after dropping scores ≥ 0.6: {len(epochs)}")