diff --git a/pysaliency/filter_datasets.py b/pysaliency/filter_datasets.py
index eef963c..429a58d 100644
--- a/pysaliency/filter_datasets.py
+++ b/pysaliency/filter_datasets.py
@@ -4,7 +4,7 @@
 
 from boltons.iterutils import chunked
 
-from .datasets import create_subset
+from .datasets import create_subset, FixationTrains, Fixations, Stimuli
 
 
 def train_split(stimuli, fixations, crossval_folds, fold_no, val_folds=1, test_folds=1, random=True, stratified_attributes=None):
@@ -232,3 +232,50 @@ def filter_stimuli_by_size(stimuli, fixations, size=None, sizes=None):
     indices = [i for i in range(len(stimuli)) if stimuli.sizes[i] in sizes]
 
     return create_subset(stimuli, fixations, indices)
+
+
+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)])
+    if invert_match is True:
+        mask = ~mask
+    indices = list(np.nonzero(mask)[0])
+    return scanpaths.filter_fixation_trains(indices)
+
+
+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)])
+    if invert_match is True:
+        mask = ~mask
+    indices = list(np.nonzero(mask)[0])
+    return fixations.filter(indices)
+
+
+def filter_stimuli_by_attribute(stimuli: Stimuli, fixations: Fixations, attribute_name, attribute_value, invert_match=False):
+    """Filter stimuli by values of attribute"""
+
+    mask = np.array([element == attribute_value for element in getattr(stimuli, attribute_name)])
+    if invert_match is True:
+        mask = ~mask
+    indices = list(np.nonzero(mask)[0])
+    return create_subset(stimuli, fixations, indices)
+
+
+def filter_scanpaths_by_lengths(scanpaths: FixationTrains, intervals: list):
+    """Filter Scanpaths by number of fixations"""
+    
+    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
+