diff --git a/.gitignore b/.gitignore
index bd08f9ba..6397ccf7 100644
--- a/.gitignore
+++ b/.gitignore
@@ -158,4 +158,6 @@ examples/utils/*
 
 sub/
 
-py_neuromodulation/data/derivatives/*
\ No newline at end of file
+py_neuromodulation/data/derivatives/*
+
+plot_0_first_demo.py
diff --git a/py_neuromodulation/nm_filter.py b/py_neuromodulation/nm_filter.py
index b4ecebb3..4070e9c1 100644
--- a/py_neuromodulation/nm_filter.py
+++ b/py_neuromodulation/nm_filter.py
@@ -1,4 +1,5 @@
 """Module for filter functionality."""
+
 import mne
 from mne.filter import _overlap_add_filter
 import numpy as np
@@ -53,17 +54,27 @@ def __init__(
             f_ranges = [f_ranges]
 
         for f_range in f_ranges:
-            filt = mne.filter.create_filter(
-                None,
-                sfreq,
-                l_freq=f_range[0],
-                h_freq=f_range[1],
-                fir_design="firwin",
-                l_trans_bandwidth=l_trans_bandwidth,  # type: ignore
-                h_trans_bandwidth=h_trans_bandwidth,  # type: ignore
-                filter_length=filter_length,  # type: ignore
-                verbose=verbose,
-            )
+            try:
+                filt = mne.filter.create_filter(
+                    None,
+                    sfreq,
+                    l_freq=f_range[0],
+                    h_freq=f_range[1],
+                    fir_design="firwin",
+                    l_trans_bandwidth=l_trans_bandwidth,  # type: ignore
+                    h_trans_bandwidth=h_trans_bandwidth,  # type: ignore
+                    filter_length=filter_length,  # type: ignore
+                    verbose=verbose,
+                )
+            except:
+                filt = mne.filter.create_filter(
+                    None,
+                    sfreq,
+                    l_freq=f_range[0],
+                    h_freq=f_range[1],
+                    fir_design="firwin",
+                    verbose=verbose,
+                )
             filter_bank.append(filt)
         self.filter_bank = np.vstack(filter_bank)
 
@@ -82,10 +93,6 @@ def filter_data(self, data: np.ndarray) -> np.ndarray:
         np.ndarray, shape (n_channels, n_fbands, n_samples)
             Filtered data.
 
-        Raises
-        ------
-        ValueError
-            If data.ndim > 2
         """
         if data.ndim > 2:
             raise ValueError(
@@ -94,15 +101,14 @@ def filter_data(self, data: np.ndarray) -> np.ndarray:
             )
         if data.ndim == 1:
             data = np.expand_dims(data, axis=0)
+
         filtered = np.array(
             [
-                [
-                    np.convolve(flt, chan, mode="same")
-                    for flt in self.filter_bank
-                ]
+                [np.convolve(self.filter_bank[0, :], chan, mode="same")]
                 for chan in data
             ]
         )
+
         return filtered
 
 
@@ -175,19 +181,19 @@ def __init__(
             phase="zero",
             fir_window="hamming",
             fir_design="firwin",
-            verbose=False
+            verbose=False,
         )
 
     def process(self, data: np.ndarray) -> np.ndarray:
         if self.filter_bank is None:
             return data
         return _overlap_add_filter(
-                x=data,
-                h=self.filter_bank,
-                n_fft=None,
-                phase="zero",
-                picks=None,
-                n_jobs=1,
-                copy=True,
-                pad="reflect_limited",
-            )
+            x=data,
+            h=self.filter_bank,
+            n_fft=None,
+            phase="zero",
+            picks=None,
+            n_jobs=1,
+            copy=True,
+            pad="reflect_limited",
+        )
diff --git a/py_neuromodulation/nm_filter_preprocessing.py b/py_neuromodulation/nm_filter_preprocessing.py
index 32010b28..3da9f784 100644
--- a/py_neuromodulation/nm_filter_preprocessing.py
+++ b/py_neuromodulation/nm_filter_preprocessing.py
@@ -5,9 +5,7 @@
 
 class PreprocessingFilter:
 
-    def __init__(
-        self, settings: dict, sfreq: int | float
-    ) -> None:
+    def __init__(self, settings: dict, sfreq: int | float) -> None:
         self.s = settings
         self.sfreq = sfreq
         self.filters = []
@@ -18,10 +16,10 @@ def __init__(
                     f_ranges=[
                         self.s["preprocessing_filter"][
                             "bandstop_filter_settings"
-                        ]["frequency_low_hz"],
+                        ]["frequency_high_hz"],
                         self.s["preprocessing_filter"][
                             "bandstop_filter_settings"
-                        ]["frequency_high_hz"],
+                        ]["frequency_low_hz"],
                     ],
                     sfreq=self.sfreq,
                     filter_length=self.sfreq - 1,
@@ -87,6 +85,7 @@ def process(self, data: np.ndarray) -> np.ndarray:
         """
 
         for filter in self.filters:
-            data = filter.filter_data(data)
-
-        return data
+            data = filter.filter_data(
+                data if len(data.shape) == 2 else data[:, 0, :]
+            )
+        return data if len(data.shape) == 2 else data[:, 0, :]
diff --git a/py_neuromodulation/nm_settings.json b/py_neuromodulation/nm_settings.json
index a16feb86..8e722220 100644
--- a/py_neuromodulation/nm_settings.json
+++ b/py_neuromodulation/nm_settings.json
@@ -4,8 +4,7 @@
     "preprocessing": [
         "raw_resampling",
         "notch_filter",
-        "re_referencing",
-        "preprocessing_filter"
+        "re_referencing"
     ],
     "documentation_preprocessing_options": [
         "raw_resampling",
@@ -38,23 +37,23 @@
     "raw_resampling_settings": {
         "resample_freq_hz": 1000
     },
-    "preprocessing_filter" : {
-        "bandstop_filter" : true,
-        "lowpass_filter" : true,
-        "highpass_filter" : true,
-        "bandpass_filter" : true,
-        "bandstop_filter_settings" : {
+    "preprocessing_filter": {
+        "bandstop_filter": true,
+        "lowpass_filter": true,
+        "highpass_filter": true,
+        "bandpass_filter": true,
+        "bandstop_filter_settings": {
             "frequency_low_hz": 100,
             "frequency_high_hz": 160
         },
-        "lowpass_filter_settings" : {
+        "lowpass_filter_settings": {
             "frequency_cutoff_hz": 200
         },
-        "highpass_filter_settings" : {
-            "frequency_cutoff_hz": 1
+        "highpass_filter_settings": {
+            "frequency_cutoff_hz": 3
         },
-        "bandpass_filter_settings" : {
-            "frequency_low_hz": 1,
+        "bandpass_filter_settings": {
+            "frequency_low_hz": 3,
             "frequency_high_hz": 200
         }
     },
diff --git a/tests/conftest.py b/tests/conftest.py
index 0022c9b6..93cae015 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1,16 +1,80 @@
 import pytest
 import numpy as np
 
-from py_neuromodulation.nm_rereference import ReReferencer
 from py_neuromodulation import (
     nm_generator,
+    nm_stream_offline,
     nm_settings,
     nm_IO,
     nm_define_nmchannels,
 )
 
+
+@pytest.fixture
+def setup_default_stream_fast_compute():
+    """This test function sets a data batch and automatic initialized M1 datafram
+
+    Args:
+        PATH_PYNEUROMODULATION (string): Path to py_neuromodulation repository
+
+    Returns:
+        ieeg_batch (np.ndarray): (channels, samples)
+        df_M1 (pd Dataframe): auto intialized table for rereferencing
+        settings_wrapper (settings.py): settings.json
+        fs (float): example sampling frequency
+    """
+
+    (
+        RUN_NAME,
+        PATH_RUN,
+        PATH_BIDS,
+        PATH_OUT,
+        datatype,
+    ) = nm_IO.get_paths_example_data()
+
+    (
+        raw,
+        data,
+        sfreq,
+        line_noise,
+        coord_list,
+        coord_names,
+    ) = nm_IO.read_BIDS_data(
+        PATH_RUN=PATH_RUN, BIDS_PATH=PATH_BIDS, datatype=datatype
+    )
+
+    nm_channels = nm_define_nmchannels.set_channels(
+        ch_names=raw.ch_names,
+        ch_types=raw.get_channel_types(),
+        reference="default",
+        bads=raw.info["bads"],
+        new_names="default",
+        used_types=("ecog", "dbs", "seeg"),
+        target_keywords=("MOV_RIGHT_CLEAN",),
+    )
+
+    settings = nm_settings.get_default_settings()
+    settings = nm_settings.reset_settings(settings)
+    settings["fooof"]["aperiodic"]["exponent"] = True
+    settings["fooof"]["aperiodic"]["offset"] = True
+    settings["features"]["fooof"] = True
+
+    stream = nm_stream_offline.Stream(
+        settings=settings,
+        nm_channels=nm_channels,
+        path_grids=None,
+        verbose=True,
+        sfreq=sfreq,
+        line_noise=line_noise,
+        coord_list=coord_list,
+        coord_names=coord_names,
+    )
+
+    return data, stream
+
+
 @pytest.fixture
-def setup():
+def setup_databatch():
     """This test function sets a data batch and automatic initialized M1 datafram
 
     Args:
diff --git a/tests/test_fooof.py b/tests/test_fooof.py
index 5dc2a559..1ed10636 100644
--- a/tests/test_fooof.py
+++ b/tests/test_fooof.py
@@ -1,79 +1,9 @@
-import pytest
+from py_neuromodulation import nm_generator
 
-from py_neuromodulation import (
-    nm_generator,
-    nm_stream_offline,
-    nm_IO,
-    nm_define_nmchannels,
-    nm_settings,
-)
 
+def test_fooof_features(setup_default_stream_fast_compute):
 
-@pytest.fixture
-def setup_stream():
-    """This test function sets a data batch and automatic initialized M1 datafram
-
-    Args:
-        PATH_PYNEUROMODULATION (string): Path to py_neuromodulation repository
-
-    Returns:
-        ieeg_batch (np.ndarray): (channels, samples)
-        df_M1 (pd Dataframe): auto intialized table for rereferencing
-        settings_wrapper (settings.py): settings.json
-        fs (float): example sampling frequency
-    """
-
-    (
-        RUN_NAME,
-        PATH_RUN,
-        PATH_BIDS,
-        PATH_OUT,
-        datatype,
-    ) = nm_IO.get_paths_example_data()
-
-    (
-        raw,
-        data,
-        sfreq,
-        line_noise,
-        coord_list,
-        coord_names,
-    ) = nm_IO.read_BIDS_data(
-        PATH_RUN=PATH_RUN, BIDS_PATH=PATH_BIDS, datatype=datatype
-    )
-
-    nm_channels = nm_define_nmchannels.set_channels(
-        ch_names=raw.ch_names,
-        ch_types=raw.get_channel_types(),
-        reference="default",
-        bads=raw.info["bads"],
-        new_names="default",
-        used_types=("ecog", "dbs", "seeg"),
-        target_keywords=("MOV_RIGHT_CLEAN",),
-    )
-
-    settings = nm_settings.get_default_settings()
-    settings = nm_settings.reset_settings(settings)
-    settings["fooof"]["aperiodic"]["exponent"] = True
-    settings["fooof"]["aperiodic"]["offset"] = True
-    settings["features"]["fooof"] = True
-
-    stream = nm_stream_offline.Stream(
-        settings=settings,
-        nm_channels=nm_channels,
-        path_grids=None,
-        verbose=True,
-        sfreq=sfreq,
-        line_noise=line_noise,
-        coord_list=coord_list,
-        coord_names=coord_names,
-    )
-
-    return data, stream
-
-def test_fooof_features(setup_stream):
-
-    data, stream = setup_stream
+    data, stream = setup_default_stream_fast_compute
 
     generator = nm_generator.raw_data_generator(
         data, stream.settings, stream.sfreq
diff --git a/tests/test_preprocessing_filter.py b/tests/test_preprocessing_filter.py
index ee88f08a..139e680b 100644
--- a/tests/test_preprocessing_filter.py
+++ b/tests/test_preprocessing_filter.py
@@ -1,16 +1,151 @@
+import numpy as np
+from scipy import signal
 
+from py_neuromodulation import nm_settings
 from py_neuromodulation.nm_filter_preprocessing import PreprocessingFilter
 
-def test_preprocessing_filter(setup):
-    ch_names, ch_types, bads, data_batch = setup
+
+def test_preprocessing_within_pipeline(setup_default_stream_fast_compute):
+
+    data, stream = setup_default_stream_fast_compute
+
+    stream.settings["preprocessing"].append("preprocessing_filter")
+
+    stream.settings["preprocessing_filter"]["bandstop_filter"] = True
+    stream.settings["preprocessing_filter"]["bandpass_filter"] = True
+    stream.settings["preprocessing_filter"]["lowpass_filter"] = True
+    stream.settings["preprocessing_filter"]["highpass_filter"] = True
+
+    stream.sfreq
+
+    try:
+        _ = stream.run(data[:, : int(stream.sfreq * 2)])
+    except Exception as e:
+        assert False, f"Error in pipeline including preprocess filtering : {e}"
+
+
+def test_preprocessing_filter_lowpass():
+
+    data_batch = np.random.random([1, 1000])
+
+    settings = nm_settings.get_default_settings()
+    settings["preprocessing"] = settings["preprocessing"].append(
+        "preprocessing_filter"
+    )
+    settings["preprocessing_filter"]["lowpass_filter"] = True
+    settings["preprocessing_filter"]["highpass_filter"] = False
+    settings["preprocessing_filter"]["bandpass_filter"] = False
+    settings["preprocessing_filter"]["bandstop_filter"] = False
+
+    settings["preprocessing_filter"]["lowpass_filter_settings"][
+        "frequency_cutoff_hz"
+    ] = 100
+
+    sfreq = 1000
+
+    preprocessing_filter = PreprocessingFilter(settings, sfreq)
+    data_filtered = preprocessing_filter.process(data_batch)
+
+    # compute a scipy signal welch to check if the filter worked
+    f, Pxx = signal.welch(data_batch, fs=sfreq, nperseg=1000)
+    f, Pxx_f = signal.welch(data_filtered, fs=sfreq, nperseg=1000)
+
+    # check if the power in the frequency range of the lowpass filter is reduced
+    assert np.mean(Pxx_f[0, 100:500]) < np.mean(Pxx[0, 100:500])
+
+
+def test_preprocessing_filter_highpass():
+
+    data_batch = np.random.random([1, 1000])
+
+    settings = nm_settings.get_default_settings()
+    settings["preprocessing"] = settings["preprocessing"].append(
+        "preprocessing_filter"
+    )
+    settings["preprocessing_filter"]["highpass_filter"] = True
+    settings["preprocessing_filter"]["lowpass_filter"] = False
+    settings["preprocessing_filter"]["bandpass_filter"] = False
+    settings["preprocessing_filter"]["bandstop_filter"] = False
+
+    settings["preprocessing_filter"]["highpass_filter_settings"][
+        "frequency_cutoff_hz"
+    ] = 100
+
+    sfreq = 1000
+
+    preprocessing_filter = PreprocessingFilter(settings, sfreq)
+    data_filtered = preprocessing_filter.process(data_batch)
+
+    # compute a scipy signal welch to check if the filter worked
+    f, Pxx = signal.welch(data_batch, fs=sfreq, nperseg=1000)
+    f, Pxx_f = signal.welch(data_filtered, fs=sfreq, nperseg=1000)
+
+    # check if the power in the frequency range of the highpass filter is reduced
+    assert np.mean(Pxx_f[0, 0:100]) < np.mean(Pxx[0, 0:100])
+
+
+def test_preprocessing_filter_bandstop():
+
+    data_batch = np.random.random([1, 1000])
 
     settings = nm_settings.get_default_settings()
-    settings = nm_settings.set_settings_fast_compute(settings)
+    settings["preprocessing"] = settings["preprocessing"].append(
+        "preprocessing_filter"
+    )
+    settings["preprocessing_filter"]["bandstop_filter"] = True
+    settings["preprocessing_filter"]["bandpass_filter"] = False
+    settings["preprocessing_filter"]["lowpass_filter"] = False
+    settings["preprocessing_filter"]["highpass_filter"] = False
+
+    settings["preprocessing_filter"]["bandstop_filter_settings"][
+        "frequency_low_hz"
+    ] = 100
+    settings["preprocessing_filter"]["bandstop_filter_settings"][
+        "frequency_high_hz"
+    ] = 160
+
+    sfreq = 1000
 
     preprocessing_filter = PreprocessingFilter(settings, sfreq)
+    data_filtered = preprocessing_filter.process(data_batch)
+
+    # compute a scipy signal welch to check if the filter worked
+    f, Pxx = signal.welch(data_batch, fs=sfreq, nperseg=1000)
+    f, Pxx_f = signal.welch(data_filtered, fs=sfreq, nperseg=1000)
+
+    # check if the power in the frequency range of the bandstop filter is reduced
+    assert np.mean(Pxx_f[0, 100:160]) < np.mean(Pxx[0, 100:160])
+
+
+def test_preprocessing_filter_bandpass():
+
+    data_batch = np.random.random([1, 1000])
 
+    settings = nm_settings.get_default_settings()
+    settings["preprocessing"] = settings["preprocessing"].append(
+        "preprocessing_filter"
+    )
+    settings["preprocessing_filter"]["bandstop_filter"] = False
+    settings["preprocessing_filter"]["bandpass_filter"] = True
+    settings["preprocessing_filter"]["lowpass_filter"] = False
+    settings["preprocessing_filter"]["highpass_filter"] = False
+
+    settings["preprocessing_filter"]["bandpass_filter_settings"][
+        "frequency_low_hz"
+    ] = 100
+    settings["preprocessing_filter"]["bandpass_filter_settings"][
+        "frequency_high_hz"
+    ] = 160
+
+    sfreq = 1000
+
+    preprocessing_filter = PreprocessingFilter(settings, sfreq)
     data_filtered = preprocessing_filter.process(data_batch)
 
-    assert data_filtered.shape == data_batch.shape
+    # compute a scipy signal welch to check if the filter worked
+    f, Pxx = signal.welch(data_batch, fs=sfreq, nperseg=1000)
+    f, Pxx_f = signal.welch(data_filtered, fs=sfreq, nperseg=1000)
 
-    assert data_filtered != data_batch
\ No newline at end of file
+    # check if the power in the frequency range of the bandpass filter is reduced
+    assert np.mean(Pxx_f[0, 0:100]) < np.mean(Pxx[0, 0:100])
+    assert np.mean(Pxx_f[0, 160:500]) < np.mean(Pxx[0, 160:500])
diff --git a/tests/test_rereference.py b/tests/test_rereference.py
index 54c5fbad..341e1f80 100644
--- a/tests/test_rereference.py
+++ b/tests/test_rereference.py
@@ -14,8 +14,8 @@
 )
 
 
-def test_rereference_not_used_channels_no_reref(setup):
-    ch_names, ch_types, bads, data_batch = setup
+def test_rereference_not_used_channels_no_reref(setup_databatch):
+    ch_names, ch_types, bads, data_batch = setup_databatch
 
     nm_channels = nm_define_nmchannels.set_channels(
         ch_names=ch_names,