diff --git a/meegkit/asr.py b/meegkit/asr.py
index 29ce33ca..f33a7b56 100755
--- a/meegkit/asr.py
+++ b/meegkit/asr.py
@@ -21,14 +21,12 @@ class ASR:
     component-based artifact removal method for removing transient or
     large-amplitude artifacts in multi-channel EEG recordings [1]_.
 
+    The key parameter of the method is ``cutoff``.
+
     Parameters
     ----------
     sfreq : float
         Sampling rate of the data, in Hz.
-
-    The following are optional parameters (the key parameter of the method is
-    the ``cutoff``):
-
     cutoff: float
         Standard deviation cutoff for rejection. X portions whose variance
         is larger than this threshold relative to the calibration data are
@@ -58,16 +56,16 @@ class ASR:
     method : {'riemann', 'euclid'}
         Method to use. If riemann, use the riemannian-modified version of
         ASR [2]_.
-    memory : float
-        Memory size (s), regulates the number of covariance matrices to store.
-    estimator : str in {'scm', 'lwf', 'oas', 'mcd'}
+    memory : float | None
+        Memory size (samples), regulates the number of covariance matrices to 
+        store.
+        If None (default), will use twice the sampling frequency.
+    estimator : {'scm', 'lwf', 'oas', 'mcd'}
         Covariance estimator (default: 'scm' which computes the sample
         covariance). Use 'lwf' if you need regularization (requires pyriemann).
 
     Attributes
     ----------
-    ``state_`` : dict
-        Initial state of the ASR filter.
     ``zi_``: array, shape=(n_channels, filter_order)
         Filter initial conditions.
     ``ab_``: 2-tuple
@@ -98,9 +96,9 @@ class ASR:
 
     """
 
-    def __init__(self, sfreq=250, cutoff=5, blocksize=100, win_len=0.5,
+    def __init__(self, *, sfreq=250, cutoff=5, blocksize=100, win_len=0.5,
                  win_overlap=0.66, max_dropout_fraction=0.1,
-                 min_clean_fraction=0.25, name="asrfilter", method="euclid",
+                 min_clean_fraction=0.25, method="euclid", memory=None,
                  estimator="scm", **kwargs):
 
         if pyriemann is None and method == "riemann":
@@ -115,7 +113,10 @@ def __init__(self, sfreq=250, cutoff=5, blocksize=100, win_len=0.5,
         self.min_clean_fraction = min_clean_fraction
         self.max_bad_chans = 0.3
         self.method = method
-        self.memory = int(2 * sfreq)  # smoothing window for covariances
+        if memory is None:
+            self.memory = int(2 * sfreq)  # smoothing window for covariances
+        else:
+            self.memory = memory
         self.sample_weight = np.geomspace(0.05, 1, num=self.memory + 1)
         self.sfreq = sfreq
         self.estimator = estimator
@@ -141,10 +142,10 @@ def fit(self, X, y=None, **kwargs):
         """Calibration for the Artifact Subspace Reconstruction method.
 
         The input to this data is a multi-channel time series of calibration
-        data. In typical uses the calibration data is clean resting EEG data of
-        data if the fraction of artifact content is below the breakdown point
+        data. In typical uses the calibration data is clean resting EEG data.
+        The fraction of artifact content should be below the breakdown point
         of the robust statistics used for estimation (50% theoretical, ~30%
-        practical). If the data has a proportion of more than 30-50% artifacts
+        practical). If the data has a proportion of more than 30-50% artifacts,
         then bad time windows should be removed beforehand. This data is used
         to estimate the thresholds that are used by the ASR processing function
         to identify and remove artifact components.
@@ -164,6 +165,12 @@ def fit(self, X, y=None, **kwargs):
             reasonably clean not less than 30 seconds (this method is typically
             used with 1 minute or more).
 
+        Returns
+        -------
+        clean : array, shape=(n_channels, n_samples)
+            Dataset with bad time periods removed.
+        sample_mask : boolean array, shape=(1, n_samples)
+            Mask of retained samples (logical array).
         """
         if X.ndim == 3:
             X = X.squeeze()
@@ -468,6 +475,9 @@ def asr_calibrate(X, sfreq, cutoff=5, blocksize=100, win_len=0.5,
         estimation (default=0.25).
     method : {'euclid', 'riemann'}
         Metric to compute the covariance matrix average.
+    estimator : {'scm', 'lwf', 'oas', 'mcd'}
+        Covariance estimator (default: 'scm' which computes the sample
+        covariance). Use 'lwf' if you need regularization (requires pyriemann).
 
     Returns
     -------
diff --git a/tests/test_asr.py b/tests/test_asr.py
index f213939d..f417c2e7 100644
--- a/tests/test_asr.py
+++ b/tests/test_asr.py
@@ -193,7 +193,7 @@ def test_asr_class(method, reref, show=False):
                 blah = ASR(method=method, estimator="scm")
                 blah.fit(raw2[:, train_idx])
 
-        asr = ASR(method=method, estimator="lwf")
+        asr = ASR(method=method, estimator="lwf", memory=int(2 * sfreq))
         asr.fit(raw2[:, train_idx])
     else:
         asr = ASR(method=method, estimator="scm")