diff --git a/meegkit/asr.py b/meegkit/asr.py
index 14b6bc21..2674e376 100755
--- a/meegkit/asr.py
+++ b/meegkit/asr.py
@@ -363,25 +363,21 @@ def clean_windows(X, sfreq, max_bad_chans=0.2, zthresholds=[-3.5, 5],
 
     # combine the three masks
     remove_mask = np.logical_or.reduce((mask1, mask2, mask3))
-    removed_wins = np.where(remove_mask)
+    removed_wins = np.where(remove_mask)[0]
 
     # reconstruct the samples to remove
     sample_maskidx = []
-    for i in range(len(removed_wins[0])):
+    for i, win in enumerate(removed_wins):
         if i == 0:
-            sample_maskidx = np.arange(
-                offsets[removed_wins[0][i]], offsets[removed_wins[0][i]] + N)
+            sample_maskidx = np.arange(offsets[win], offsets[win] + N)
         else:
-            sample_maskidx = np.vstack((
-                sample_maskidx,
-                np.arange(offsets[removed_wins[0][i]],
-                          offsets[removed_wins[0][i]] + N)
-            ))
+            sample_maskidx = np.r_[(sample_maskidx,
+                                    np.arange(offsets[win], offsets[win] + N))]
 
     # delete the bad chunks from the data
     sample_mask2remove = np.unique(sample_maskidx)
     if sample_mask2remove.size:
-        clean = np.delete(X, sample_mask2remove, 1)
+        clean = np.delete(X, sample_mask2remove, axis=1)
         sample_mask = np.ones((1, ns), dtype=bool)
         sample_mask[0, sample_mask2remove] = False
     else:
diff --git a/meegkit/dss.py b/meegkit/dss.py
index 1a1bd0f5..f408951c 100644
--- a/meegkit/dss.py
+++ b/meegkit/dss.py
@@ -21,7 +21,7 @@ def dss1(X, weights=None, keep1=None, keep2=1e-12):
     keep1: int
         Number of PCs to retain in function:`dss0` (default=all).
     keep2: float
-        Ignore PCs smaller than keep2 in function:`dss0` (default=10^-12).
+        Ignore PCs smaller than keep2 in function:`dss0` (default=1e-12).
 
     Returns
     -------
@@ -35,7 +35,7 @@ def dss1(X, weights=None, keep1=None, keep2=1e-12):
         Power per component (averaged).
 
     """
-    n_samples, n_chans, n_trials = theshapeof(X)
+    n_trials = theshapeof(X)[-1]
 
     # if demean: # remove weighted mean
     #   X = demean(X, weights)
diff --git a/meegkit/ress.py b/meegkit/ress.py
index 5d9d916e..dbb33a84 100644
--- a/meegkit/ress.py
+++ b/meegkit/ress.py
@@ -7,7 +7,7 @@
 
 def RESS(X, sfreq: int, peak_freq: float, neig_freq: float = 1,
          peak_width: float = .5, neig_width: float = 1, n_keep: int = 1,
-         return_maps: bool = False):
+         gamma: float = 0.01, return_maps: bool = False):
     """Rhythmic Entrainment Source Separation.
 
     As described in [1]_.
@@ -29,6 +29,10 @@ def RESS(X, sfreq: int, peak_freq: float, neig_freq: float = 1,
         FWHM of the neighboring frequencies (default=1).
     n_keep : int
         Number of components to keep (default=1). -1 keeps all components.
+    gamma : float
+        Regularization coefficient, between 0 and 1 (default=0.01, which
+        corresponds to 1 % regularization and helps reduce numerical problems
+        for noisy or reduced-rank matrices [2]_).
     return_maps : bool
         If True, also output mixing (to_ress) and unmixing matrices
         (from_ress), used to transform the data into RESS component space and
@@ -67,6 +71,9 @@ def RESS(X, sfreq: int, peak_freq: float, neig_freq: float = 1,
     .. [1] Cohen, M. X., & Gulbinaite, R. (2017). Rhythmic entrainment source
        separation: Optimizing analyses of neural responses to rhythmic sensory
        stimulation. Neuroimage, 147, 43-56.
+    .. [2] Cohen, M. X. (2021). A tutorial on generalized eigendecomposition
+       for source separation in multichannel electrophysiology.
+       ArXiv:2104.12356 [Eess, q-Bio].
 
     """
     n_samples, n_chans, n_trials = theshapeof(X)
@@ -82,8 +89,12 @@ def RESS(X, sfreq: int, peak_freq: float, neig_freq: float = 1,
                              fwhm=neig_width, n_harm=1))
     c1, _ = tscov(gaussfilt(X, sfreq, peak_freq, fwhm=peak_width, n_harm=1))
 
+    # add 1% regularization to avoid numerical precision problems in the GED
+    c0 = (c01 + c02) / 2
+    c0 = c0 * (1 - gamma) + gamma * np.trace(c0) / len(c0) * np.eye(len(c0))
+
     # perform generalized eigendecomposition
-    d, to_ress = linalg.eig(c1, (c01 + c02) / 2)
+    d, to_ress = linalg.eigh(c1, c0)
     d = d.real
     to_ress = to_ress.real
 
diff --git a/tests/test_ress.py b/tests/test_ress.py
index 0bbad244..d5c39d41 100644
--- a/tests/test_ress.py
+++ b/tests/test_ress.py
@@ -156,4 +156,4 @@ def test_ress(target, n_trials, peak_width, neig_width, neig_freq, show=False):
 if __name__ == '__main__':
     import pytest
     pytest.main([__file__])
-    # test_ress(12, 20, 1, 1, 1, show=True)
+    # test_ress(20, 16, 1, 1, 1, show=False)