diff --git a/pynapple/process/perievent.py b/pynapple/process/perievent.py
index efc03508..ac5e5b1e 100644
--- a/pynapple/process/perievent.py
+++ b/pynapple/process/perievent.py
@@ -2,7 +2,7 @@
 # @Author: gviejo
 # @Date:   2022-01-30 22:59:00
 # @Last Modified by:   Guillaume Viejo
-# @Last Modified time: 2024-01-25 16:46:51
+# @Last Modified time: 2024-01-26 15:52:19
 
 import numpy as np
 
@@ -200,8 +200,8 @@ def compute_event_trigger_average(
     time_unit="s",
 ):
     """
-    Bin the spike train in binsize and compute the Event Trigger Average (ETA) within windowsize.
-    If C is the spike count matrix and `feature` is a Tsd array, the function computes
+    Bin the event timestamps within binsize and compute the Event Trigger Average (ETA) within windowsize.
+    If C is the event count matrix and `feature` is a Tsd array, the function computes
     the Hankel matrix H from windowsize=(-t1,+t2) by offseting the Tsd array.
 
     The ETA is then defined as the dot product between H and C divided by the number of events.
diff --git a/pynapple/process/tuning_curves.py b/pynapple/process/tuning_curves.py
index f9356210..3311c119 100644
--- a/pynapple/process/tuning_curves.py
+++ b/pynapple/process/tuning_curves.py
@@ -116,7 +116,7 @@ def compute_1d_tuning_curves(group, feature, nb_bins, ep=None, minmax=None):
     if ep is None:
         ep = feature.time_support
     else:
-        assert isinstance(ep, nap.IntervalSet), "ep should be an IntervalSet"        
+        assert isinstance(ep, nap.IntervalSet), "ep should be an IntervalSet"
 
     if minmax is None:
         bins = np.linspace(np.min(feature), np.max(feature), nb_bins + 1)