diff --git a/nideconv/regressors.py b/nideconv/regressors.py
index 6a25d20..9ca00e2 100644
--- a/nideconv/regressors.py
+++ b/nideconv/regressors.py
@@ -293,8 +293,6 @@ def create_design_matrix(self, oversample=1):
 
         L = self.get_basis_function(oversample)
 
-        effective_sample_rate = self.sample_rate * oversample
-
         columns = pd.MultiIndex.from_product(([self.name], self.covariates.columns, L.columns),
                                              names=['event_type', 'covariate', 'regressor'])
         oversampled_timepoints = np.linspace(0, 
diff --git a/nideconv/response_fitter.py b/nideconv/response_fitter.py
index 2e47ff1..58caeed 100644
--- a/nideconv/response_fitter.py
+++ b/nideconv/response_fitter.py
@@ -80,7 +80,11 @@ def add_confounds(self, name, confound):
         self._add_regressor(confound)
 
 
-    def _add_regressor(self, regressor, oversample=1):
+    def _add_regressor(self, regressor, oversample=None):
+
+        if oversample is None:
+            oversample = self.oversample_design_matrix
+
         regressor.create_design_matrix(oversample=oversample)
 
         if self.X.shape[1] == 0: