diff --git a/pysaliency/filter_datasets.py b/pysaliency/filter_datasets.py
index 429a58d..8b369b0 100644
--- a/pysaliency/filter_datasets.py
+++ b/pysaliency/filter_datasets.py
@@ -236,31 +236,37 @@ def filter_stimuli_by_size(stimuli, fixations, size=None, sizes=None):
 
 def filter_scanpaths_by_attribute(scanpaths:  FixationTrains, attribute_name, attribute_value, invert_match=False):
     """Filter Scanpaths by values of scanpath attribute (fixation_trains.scanpath_attributes)"""
-    
-    mask = np.array([element == attribute_value for element in getattr(scanpaths, attribute_name)])
+
+    mask = scanpaths.scanpath_attributes[attribute_name] == attribute_value
     if invert_match is True:
         mask = ~mask
-    indices = list(np.nonzero(mask)[0])
-    return scanpaths.filter_fixation_trains(indices)
+    if mask.ndim>1:
+        mask = np.all(mask, axis=1)
+        
+    return scanpaths.filter_fixation_trains(mask)
 
 
 def filter_fixations_by_attribute(fixations: Fixations, attribute_name, attribute_value, invert_match=False):
     """Filter Fixations by values of attribute (fixations.__attributes__)"""
 
-    mask = np.array([element == attribute_value for element in getattr(fixations, attribute_name)])
+    mask = np.asarray(getattr(fixations, attribute_name)) == attribute_value
     if invert_match is True:
         mask = ~mask
-    indices = list(np.nonzero(mask)[0])
-    return fixations.filter(indices)
-
+    if mask.ndim>1:
+        mask = np.all(mask, axis=1)
+        
+    return fixations[mask]
 
 def filter_stimuli_by_attribute(stimuli: Stimuli, fixations: Fixations, attribute_name, attribute_value, invert_match=False):
-    """Filter stimuli by values of attribute"""
+    """Filter stimuli by values of attribute (stimuli.attributes)"""
 
-    mask = np.array([element == attribute_value for element in getattr(stimuli, attribute_name)])
+    mask = np.asarray(stimuli.attributes[attribute_name]) == attribute_value
     if invert_match is True:
         mask = ~mask
+    if mask.ndim>1:
+        mask = np.all(mask, axis=1)
     indices = list(np.nonzero(mask)[0])
+    
     return create_subset(stimuli, fixations, indices)
 
 
@@ -269,13 +275,12 @@ def filter_scanpaths_by_lengths(scanpaths: FixationTrains, intervals: list):
     
     intervals = _check_intervals(intervals, type=int)
     mask = np.zeros(len(scanpaths.train_lengths), dtype=bool)
-
     for n1, n2 in intervals:
         temp_mask = np.logical_and(scanpaths.train_lengths>=n1,scanpaths.train_lengths<=n2)
         mask = np.logical_or(mask, temp_mask)
-
     indices = list(np.nonzero(mask)[0])
+    
     scanpaths = scanpaths.filter_fixation_trains(indices)
-
+    
     return scanpaths