diff --git a/config.yaml b/config.yaml
index d52804fe..7a82190a 100644
--- a/config.yaml
+++ b/config.yaml
@@ -27,8 +27,8 @@ TIME_SEGMENTS: &time_segments
   TAILORED_EVENTS: # Only relevant if TYPE=EVENT
     COMPUTE: True
     SEGMENTING_METHOD: "stress_event" # 30_before, 90_before, stress_event
-    INTERVAL_OF_INTEREST: 5 # duration of event of interest [minutes]
-    INTERVAL_OF_INTEREST_PADDING: 7.5 # interval of interest padding (before and after IOI) [minutes]
+    INTERVAL_OF_INTEREST: 10 # duration of event of interest [minutes]
+    IOI_ERROR_TOLERANCE: 5 # interval of interest erorr tolerance (before and after IOI) [minutes]
 
 # See https://www.rapids.science/latest/setup/configuration/#timezone-of-your-study
 TIMEZONE: 
diff --git a/src/features/phone_esm/straw/process_user_event_related_segments.py b/src/features/phone_esm/straw/process_user_event_related_segments.py
index cd091b2c..03eeb052 100644
--- a/src/features/phone_esm/straw/process_user_event_related_segments.py
+++ b/src/features/phone_esm/straw/process_user_event_related_segments.py
@@ -124,7 +124,7 @@ def extract_ers(esm_df):
         """
 
         ioi = config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["INTERVAL_OF_INTEREST"] * 60 # interval of interest in seconds
-        ioi_padding = config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["INTERVAL_OF_INTEREST_PADDING"]  * 60 # interval of interest padding in seconds 
+        ioi_error_tolerance = config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["IOI_ERROR_TOLERANCE"]  * 60 # interval of interest error tolerance in seconds 
 
         # Get and join required data
         extracted_ers = esm_df.groupby(["device_id", "esm_session"])['timestamp'].apply(lambda x: math.ceil((x.max() - x.min()) / 1000)).reset_index().rename(columns={'timestamp': 'session_length'}) # questionnaire length
@@ -154,12 +154,13 @@ def extract_ers(esm_df):
 
         # Filter-out the sessions that are not useful. Because of the ambiguity this excludes: 
         # (1) straw event times that are marked as "0 - I don't remember"
-        # (2) straw event durations that are marked as "0 - I don't remember" 
-        extracted_ers = extracted_ers[(~extracted_ers.se_time.astype(str).str.startswith("0 - ")) & (~extracted_ers.se_duration.astype(str).str.startswith("0 - "))]
+        extracted_ers = extracted_ers[~extracted_ers.se_time.astype(str).str.startswith("0 - ")]
         extracted_ers.reset_index(drop=True, inplace=True)
 
+        extracted_ers.loc[extracted_ers.se_duration.astype(str).str.startswith("0 - "), 'se_duration'] = 0
+
         # Add default duration in case if participant answered that no stressful event occured
-        extracted_ers["se_duration"] = extracted_ers["se_duration"].fillna(int((ioi + 2*ioi_padding) * 1000))
+        extracted_ers["se_duration"] = extracted_ers["se_duration"].fillna(int((ioi + 2*ioi_error_tolerance) * 1000))
 
         # Prepare data to fit the data structure in the CSV file ...
         # Add the event time as the end of the questionnaire if no stress event occured
@@ -183,20 +184,22 @@ def extract_ers(esm_df):
         extracted_ers['se_duration'] = \
             extracted_ers['se_duration'].apply(lambda x: math.ceil(x / 1000) if isinstance(x, int) else (pd.to_datetime(x).hour * 60 + pd.to_datetime(x).minute) * 60)
 
-        # Check whether min se_duration is at least the same duration as the ioi. Filter-out the rest.
-        extracted_ers = extracted_ers[extracted_ers["se_duration"] >= ioi].reset_index(drop=True)
+        # Check explicitley whether min duration is at least 0. This will eliminate rows that would be investigated after the end of the questionnaire.
+        extracted_ers = extracted_ers[extracted_ers['session_end_timestamp'] - extracted_ers['event_timestamp'] >= 0]
+        # Double check whether min se_duration is at least 0. Filter-out the rest. Negative values are considered invalid.
+        extracted_ers = extracted_ers[extracted_ers["se_duration"] >= 0].reset_index(drop=True)
 
         """>>>>> end section <<<<<"""
 
-        # Simply override all remaining durations to be of an equal amount
-        extracted_ers['se_duration'] = ioi + 2*ioi_padding 
+        # Simply override all durations to be of an equal amount
+        extracted_ers['se_duration'] = ioi + 2*ioi_error_tolerance 
 
-        # 
+        # If target is 0 then shift by the total stress event duration, otherwise shift it by ioi_tolerance
         extracted_ers['shift'] = \
             np.where(
                 extracted_ers['appraisal_stressfulness_event'] == 0,
                 extracted_ers['se_duration'], 
-                ioi_padding
+                ioi_error_tolerance
             )
 
         extracted_ers['shift'] = extracted_ers['shift'].apply(lambda x: format_timestamp(int(x)))
@@ -214,7 +217,7 @@ def extract_ers(esm_df):
     else:
         raise Exception("Please select correct target method for the event-related segments.")
         extracted_ers = pd.DataFrame(columns=["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"])
-    
+
     return extracted_ers[["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"]]