diff --git a/pynapple/process/signal_processing.py b/pynapple/process/signal_processing.py
new file mode 100644
index 00000000..038f0394
--- /dev/null
+++ b/pynapple/process/signal_processing.py
@@ -0,0 +1,67 @@
+"""
+	Signal processing module
+"""
+
+import numpy as np
+from .. import core as nap
+from scipy.signal import butter, lfilter, filtfilt
+
+
+def _butter_bandpass(lowcut, highcut, fs, order=5):
+    nyq = 0.5 * fs
+    low = lowcut / nyq
+    high = highcut / nyq
+    b, a = butter(order, [low, high], btype='band')
+    return b, a
+
+def _butter_bandpass_filter(data, lowcut, highcut, fs, order=4):
+    b, a = _butter_bandpass(lowcut, highcut, fs, order=order)
+    y = lfilter(b, a, data)
+    return y
+
+def compute_bandpass_filter(data, freq_band, sampling_frequency=None, order=4):
+    """
+    Bandpass filtering the LFP.
+    
+    Parameters
+    ----------
+    data : Tsd/TsdFrame
+        Description
+    lowcut : TYPE
+        Description
+    highcut : TYPE
+        Description
+    fs : TYPE
+        Description
+    order : int, optional
+        Description
+    
+    Raises
+    ------
+    RuntimeError
+        Description
+    """
+    time_support = data.time_support
+    time_index = data.as_units('s').index.values
+    if type(data) is nap.TsdFrame:
+        tmp = np.zeros(data.shape)
+        for i in np.arange(data.shape[1]):
+            tmp[:,i] = bandpass_filter(data[:,i], lowcut, highcut, fs, order)
+
+        return nap.TsdFrame(
+            t = time_index,
+            d = tmp,
+            time_support = time_support,
+            time_units = 's',
+            columns = data.columns)
+
+    elif type(data) is nap.Tsd:
+        flfp = _butter_bandpass_filter(data.values, lowcut, highcut, fs, order)
+        return nap.Tsd(
+            t=time_index,
+            d=flfp,
+            time_support=time_support,
+            time_units='s')
+
+    else:
+        raise RuntimeError("Unknow format. Should be Tsd/TsdFrame")
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