diff --git a/src/spikeinterface/preprocessing/whiten.py b/src/spikeinterface/preprocessing/whiten.py
index 54f6e0e903..ec3a3e91a9 100644
--- a/src/spikeinterface/preprocessing/whiten.py
+++ b/src/spikeinterface/preprocessing/whiten.py
@@ -15,27 +15,27 @@ class WhitenRecording(BasePreprocessor):
     ----------
     recording: RecordingExtractor
         The recording extractor to be whitened.
-    dtype: None or dtype
+    dtype: None or dtype, default: None
         If None the the parent dtype is kept.
         For integer dtype a int_scale must be also given.
-    mode: 'global' / 'local'
+    mode: 'global' / 'local', default: 'global'
         'global' use the entire covariance matrix to compute the W matrix
         'local' use local covariance (by radius) to compute the W matrix
-    radius_um: None or float
+    radius_um: None or float, default: None
         Used for mode = 'local' to get the neighborhood
-    apply_mean: bool
+    apply_mean: bool, default: False
         Substract or not the mean matrix M before the dot product with W.
-    int_scale : None or float
+    int_scale : None or float, default: None
         Apply a scaling factor to fit the integer range.
         This is used when the dtype is an integer, so that the output is scaled.
         For example, a value of `int_scale=200` will scale the traces value to a standard deviation of 200.
-    eps : float, default 1e-8
+    eps : float or None, default: None
         Small epsilon to regularize SVD.
-        If None, eps is estimated from the data. If the data is float type and scaled down to very small values,
-        then the eps is automatically set to a small fraction of the median of the squared data.
-    W : 2d np.array
-        Pre-computed whitening matrix, by default None
-    M : 1d np.array or None
+        If None, eps is default to 1e-8. If the data is float type and scaled down to very small values,
+        then the eps is automatically set to a small fraction (1e-3) of the median of the squared data.
+    W : 2d np.array, default: None
+        Pre-computed whitening matrix
+    M : 1d np.array or None, default: None
         Pre-computed means.
         M can be None when previously computed with apply_mean=False
     **random_chunk_kwargs : Keyword arguments for `spikeinterface.core.get_random_data_chunk()` function
@@ -56,7 +56,7 @@ def __init__(
         mode="global",
         radius_um=100.0,
         int_scale=None,
-        eps=1e-8,
+        eps=None,
         W=None,
         M=None,
         **random_chunk_kwargs,
@@ -127,7 +127,7 @@ def get_traces(self, start_frame, end_frame, channel_indices):
 whiten = define_function_from_class(source_class=WhitenRecording, name="whiten")
 
 
-def compute_whitening_matrix(recording, mode, random_chunk_kwargs, apply_mean, radius_um=None, eps=1e-8):
+def compute_whitening_matrix(recording, mode, random_chunk_kwargs, apply_mean, radius_um=None, eps=None):
     """
     Compute whitening matrix
 
@@ -145,10 +145,11 @@ def compute_whitening_matrix(recording, mode, random_chunk_kwargs, apply_mean, r
         Keyword arguments for  get_random_data_chunks()
     apply_mean : bool
         If True, the mean is removed prior to computing the covariance
-    radius_um : float, optional
-        Used for mode = 'local' to get the neighborhood, by default None
-    eps : float, optional
-        Small epsilon to regularize SVD, by default 1e-8
+    radius_um : float, default: None
+        Used for mode = 'local' to get the neighborhood
+    eps : float, default: None
+        Small epsilon to regularize SVD. If None, the default is set to 1e-8, but if the data is float type and scaled
+        down to very small values, eps is automatically set to a small fraction (1e-3) of the median of the squared data.
 
     Returns
     -------
@@ -180,7 +181,9 @@ def compute_whitening_matrix(recording, mode, random_chunk_kwargs, apply_mean, r
     # whitening. We therefore check to see if the data is float
     # type and we estimate a more reasonable eps in the case
     # where the data is on a scale less than 1.
-    if data.dtype.kind == "f" or eps is None:
+    if eps is None:
+        eps = 1e-8
+    if data.dtype.kind == "f":
         median_data_sqr = np.median(data**2)  # use the square because cov (and hence S) scales as the square
         if median_data_sqr < 1 and median_data_sqr > 0:
             eps = max(1e-16, median_data_sqr * 1e-3)  # use a small fraction of the median of the squared data