StreamReceiver: real-time buffer filtered with a causal filter#

A StreamReceiver can be used to create a data buffer on which different operations have already been applied. For instance, a buffer where data is filtered with a bandpass filter.

# Authors: Mathieu Scheltienne <>
# License: LGPL-2.1


Both StreamPlayer and StreamRecorder create a new process to stream or record data. On Windows, mutliprocessing suffers a couple of restrictions. The entry-point of a multiprocessing program should be protected with if __name__ == '__main__': to ensure it can safely import and run the module. More information on the documentation for multiprocessing on Windows.

This example will use a sample EEG resting-state dataset that can be retrieve with bsl.datasets. The dataset is stored in the user home directory in the folder bsl_data (e.g. C:\Users\User\bsl_data).

from math import ceil
import time

from matplotlib import pyplot as plt
import mne
import numpy as np
from scipy.signal import butter, sosfilt, sosfilt_zi

from bsl import StreamReceiver, StreamPlayer, datasets
from bsl.utils import Timer

To simulate an actual signal coming from an LSL stream, a StreamPlayer is used with a 40 seconds resting-state recording. This dataset is already filtered between (1, 40) Hz.


<Player: StreamPlayer | ON | /Users/scheltie/bsl_data/eeg_sample/resting_state-raw.fif>
raw =, preload=False, verbose=False)
print (f"BP filter between: {['highpass']}, {['lowpass']} Hz")


BP filter between: 1.0, 40.0 Hz


Data should be filtered along one dimension. For this example, a butter IIR filter is used. More information on filtering is available on the MNE documentation:

def create_bandpass_filter(low, high, fs, n):
    Create a bandpass filter using a butter filter of order n.

    low : float
        The lower pass-band edge.
    high : float
        The upper pass-band edge.
    fs : float
        Sampling rate of the data.
    n : int
        Order of the filter.

    sos : array
        Second-order sections representation of the IIR filter.
    zi_coeff : array
        Initial condition for sosfilt for step response steady-state.
    # Divide by the Nyquist frequency
    bp_low = low / (0.5 * fs)
    bp_high = high / (0.5 * fs)
    # Compute SOS output (second order sections)
    sos = butter(n, [bp_low, bp_high], btype='band', output='sos')
    # Construct initial conditions for sosfilt for step response steady-state.
    zi_coeff = sosfilt_zi(sos).reshape((sos.shape[0], 2, 1))

    return sos, zi_coeff

EEG data is usually subject to a lage DC offset, which corresponds to a step response steady-state. The initial conditions are determined by multiplying the zi_coeff with the DC offset. The DC offset value can be approximated by taking the mean of a small window.


When creating the filtered buffer, the duration has to be define to create a numpy array of the correct shape and pre-allocate the required space.

buffer_duration = 5  # in seconds

Then, a StreamReceiver is created. But the actual buffer and window size of the StreamReceiver are set as small as possible. The buffer from the StreamReceiver is only used to store the last samples until they are retrieved, filtered, and added to the filtered buffer. In this example, the StreamReceiver buffer and window size are set to 200 ms.

sr = StreamReceiver(bufsize=0.2, winsize=0.2, stream_name=stream_name)
time.sleep(0.2)  # wait to fill LSL inlet.


A StreamReceiver opens an LSL inlet for each connected stream at initialization. The inlet’s buffer is empty when created and fills up as time passes. Data is pulled from the LSL inlet each time acquire is called.

The filtered buffer can be define as a class that uses the elements created previously. The method .update() pulls new samples from the LSL stream, filters and add them to the buffer while removing older samples that are now exiting the buffer.

The StreamReceiver appends a TRIGGER channel at the beginning of the data array. For filtering, the trigger channel is not needed. Thus, the number of channels is reduced by 1 and the first channel on the (samples, channels) data array is ignored.

For this example, the filter is defined between 5 and 10 Hz to emphasize its effect as the dataset streamed is already filtered between (1, 40) Hz.

class Buffer:
    A buffer containing filter data and its associated timestamps.

    buffer_duration : float
        Length of the buffer in seconds.
    sr : bsl.StreamReceiver
        StreamReceiver connected to the desired data stream.

    def __init__(self, buffer_duration, sr):
        # Store the StreamReceiver in a class attribute = sr

        # Retrieve sampling rate and number of channels
        self.fs = int([stream_name].sample_rate)
        self.nb_channels = len([stream_name].ch_list) - 1

        # Define duration
        self.buffer_duration = buffer_duration
        self.buffer_duration_samples = ceil(self.buffer_duration * self.fs)

        # Create data array
        self.timestamps = np.zeros(self.buffer_duration_samples) = np.zeros((self.buffer_duration_samples, self.nb_channels))
        # For demo purposes, let's store also the raw data
        self.raw_data = np.zeros((self.buffer_duration_samples,

        # Create filter BP (1, 15) Hz and filter variables
        self.sos, self.zi_coeff = create_bandpass_filter(5., 10., self.fs, n=2)
        self.zi = None

    def update(self):
        Update the buffer with new samples from the StreamReceiver. This method
        should be called regularly, with a period at least smaller than the
        StreamReceiver buffer length.
        # Acquire new data points
        data_acquired, ts_list =

        if len(ts_list) == 0:
            return  # break early, no new samples

        # Remove trigger channel
        data_acquired = data_acquired[:, 1:]

        # Filter acquired data
        if self.zi is None:
            # Initialize the initial conditions for the cascaded filter delays.
            self.zi = self.zi_coeff * np.mean(data_acquired, axis=0)
        data_filtered, self.zi = sosfilt(self.sos, data_acquired, axis=0,

        # Roll buffer, remove samples exiting and add new samples
        self.timestamps = np.roll(self.timestamps, -len(ts_list))
        self.timestamps[-len(ts_list):] = ts_list = np.roll(, -len(ts_list), axis=0)[-len(ts_list):, :] = data_filtered
        self.raw_data = np.roll(self.raw_data, -len(ts_list), axis=0)
        self.raw_data[-len(ts_list):, :] = data_acquired

Testing the filtered buffer#

The filtered buffer must be updated regularly. In this example, the StreamReceiver buffer has been initialized at 200 ms. Thus, the filtered buffer should be updated at most every 200 ms, else, there is a risk that a couple of samples will be missed between 2 updates.

A 15 seconds acquisition is used to test the buffer. Every 5 seconds, the buffer is retrieved and plotted.

# Create plot
f, ax = plt.subplots(2, 1, sharex=True)
ax[0].set_title('Raw data')
ax[1].set_title('Filtered data between (5, 10) Hz')
# Create buffer
buffer = Buffer(buffer_duration, sr)

# Start by filling once the entire buffer (to get rid of the initialization 0)
timer = Timer()
while timer.sec() <= buffer_duration:

# Acquire during 15 seconds and plot every 5 seconds
idx_last_plot = 1
while timer.sec() <= 15:
    # check if we just passed the 5s between plot limit
    if timer.sec() // 5 == idx_last_plot:
        # average all channels to simulate an evoked response
        ax[0].plot(buffer.timestamps, np.mean(buffer.raw_data[:, 1:], axis=1))
        ax[1].plot(buffer.timestamps, np.mean([:, 1:], axis=1))
        idx_last_plot += 1
Raw data, Filtered data between (5, 10) Hz

Stop the mock LSL stream.

del sr  # disconnects and close the LSL inlet.

Total running time of the script: ( 0 minutes 22.646 seconds)

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