diff --git a/bidscoin/bids.py b/bidscoin/bids.py
index defbeeaf..2df14370 100644
--- a/bidscoin/bids.py
+++ b/bidscoin/bids.py
@@ -117,8 +117,8 @@ def eventstable(self) -> pd.DataFrame:
             for column, value in self.time['start'].items():
                 start &= (self.logtable[column].astype(str) == str(value)).values
             if start.any():
-                LOGGER.bcdebug(f"Reseting clock offset of: {df['onset'][start.values].iloc[0]}")
-                df['onset'] = df['onset'] - df['onset'][start.values].iloc[0]  # Take the time of the first occurrence as zero
+                LOGGER.bcdebug(f"Resetting clock offset: {df['onset'][start.values].iloc[0]}")
+                df['onset'] -= df['onset'][start.values].iloc[0]  # Take the time of the first occurrence as zero
 
         # Loop over the row groups to filter/edit the rows
         rows = pd.Series([len(self.rows) == 0] * len(df)).astype(bool)  # Boolean series with True values if no row expressions were specified
@@ -197,14 +197,14 @@ def is_float(s):
             valid = False
 
         # Check if the logtable has existing and unique column names
-        df = self.logtable
+        columns = self.logtable.columns
         for name in set([name for item in self.columns for name in item.values()] + [name for item in self.rows for name in item['include'].keys()] +
                         [*self.time.get('start',{}).keys()] + self.time.get('cols',[])):
-            if name and name not in df:
+            if name and name not in columns:
                 LOGGER.warning(f"Column '{name}' not found in the event table of {self}")
                 valid = False
-        if not df.columns[df.columns.duplicated()].empty:
-            LOGGER.warning(f"Duplicate columns found in: {df.columns}\n{self}")
+        if columns.duplicated().any():
+            LOGGER.warning(f"Duplicate columns found: {columns}\n{self}")
             valid = False
 
         return valid
diff --git a/bidscoin/heuristics/bidsmap_dccn.yaml b/bidscoin/heuristics/bidsmap_dccn.yaml
index 1acad4b5..e0795e33 100644
--- a/bidscoin/heuristics/bidsmap_dccn.yaml
+++ b/bidscoin/heuristics/bidsmap_dccn.yaml
@@ -40,6 +40,7 @@ Options:
       fallback: y                   # Appends unhandled dcm2niix suffixes to the `acq` label if 'y' (recommended, else the suffix data is discarded)
     events2bids:
       table: event                  # The table that is used to generate the output table (https://www.neurobs.com/pres_docs/html/03_presentation/07_data_reporting/01_logfiles/index.html)
+      skiprows: 3                   # The number of (header) rows that precede the actual table data
       meta: [.json, .tsv]
 
 
diff --git a/bidscoin/plugins/events2bids.py b/bidscoin/plugins/events2bids.py
index e0f8e0f8..6a00bb5b 100644
--- a/bidscoin/plugins/events2bids.py
+++ b/bidscoin/plugins/events2bids.py
@@ -13,7 +13,7 @@
 LOGGER = logging.getLogger(__name__)
 
 # The default/fallback options that are set when installing/using the plugin
-OPTIONS = Plugin({'table': 'event', 'meta': ['.json', '.tsv']})  # The file extensions of the equally named metadata sourcefiles that are copied over as BIDS sidecar files
+OPTIONS = Plugin({'table': 'event', 'skiprows': 3, 'meta': ['.json', '.tsv']})  # The file extensions of the equally named metadata sourcefiles that are copied over as BIDS sidecar files
 
 
 def test(options: Plugin=OPTIONS) -> int:
@@ -240,9 +240,9 @@ def __init__(self, sourcefile: Path, _data: dict, options: dict):
         super().__init__(sourcefile, _data, options)
 
         # Read the log-tables from the Presentation logfile
-        self._sourcetable = pd.read_csv(self.sourcefile, sep='\t', skiprows=3, skip_blank_lines=True)
+        self._sourcetable = pd.read_csv(self.sourcefile, sep='\t', skiprows=options.get('skiprows',3), skip_blank_lines=True)
         """The Presentation log-tables (https://www.neurobs.com/pres_docs/html/03_presentation/07_data_reporting/01_logfiles/index.html)"""
-        self._columns     = self._sourcetable.columns
+        self._sourcecols  = self._sourcetable.columns
         """Store the original column names"""
 
     @property
@@ -256,7 +256,7 @@ def logtable(self) -> pd.DataFrame:
         survey_header   = (df.iloc[:, 0] == 'Time').idxmax() or nrows
 
         # Get the row indices to slice the event, stimulus, video or survey table
-        df.columns = self._columns
+        df.columns = self._sourcecols
         if self.options['table'] == 'event':
             begin = 0
             end   = min(stimulus_header, video_header, survey_header)
@@ -277,9 +277,7 @@ def logtable(self) -> pd.DataFrame:
             end   = nrows
             LOGGER.error(f"NOT IMPLEMENTED TABLE: {self.options['table']}")
 
-        LOGGER.bcdebug(f"Slicing '{self.options['table']}{df.shape}' sourcetable[{begin}:{end}]")
-
-        # Ensure unique column names by renaming columns with NaN or empty names and by appending suffixes to duplicate names
+        # Ensure unique column names by renaming columns with NaN or empty names, and by appending suffixes to duplicate names
         cols = []                               # The new column names
         dupl = {}                               # The duplicate index number
         for i, col in enumerate(df.columns):
@@ -296,4 +294,5 @@ def logtable(self) -> pd.DataFrame:
         df.columns = cols
 
         # Return the sliced the table
+        LOGGER.bcdebug(f"Slicing '{self.options['table']}{df.shape}' sourcetable[{begin}:{end}]")
         return df.iloc[begin:end]