Stream Viewer#

Visualize real-time EEG data from Wearable Sensing devices using MNE-LSL’s StreamViewer.

Launch LSL Stream Viewer#

MNE-LSL StreamViewer

StreamViewer displaying real-time EEG with eyes-closed alpha activity.#

Once your LSL stream is running (see LSL Setup), use the following code with the stream name to start StreamViewer and visualize the data in real-time:

Launch StreamViewer for real-time DSI visualization#
from mne_lsl.stream_viewer import StreamViewer

stream_name = "WS-default"  # Or DSI-24, DSI-VR300, WS-default

viewer = StreamViewer(stream_name=stream_name)
viewer.start()

Finding Your Stream Name

If unsure of your stream name, check the LSL GUI or use stream discovery to list available streams.

Customize the Viewer by Channel and Duration#

Channel selection and time window duration are adjusted interactively through the StreamViewer GUI after launch. There are no constructor parameters for these in v1.12.0.

View Filtered Streams from StreamLSL#

Note

In mne-lsl 1.12.0, StreamViewer connects directly to a raw LSL stream by name. It does not accept a pre-filtered StreamLSL object. For filtered visualization, apply filters via StreamLSL and retrieve data with get_data() to plot with your preferred plotting library.

To use StreamLSL for filtered data processing alongside the viewer, run them as separate scripts or processes — StreamViewer.start() is blocking and calls sys.exit() when the window closes, so code after it will not execute.

Script 1 — Filtered data acquisition with StreamLSL#
from mne_lsl.stream import StreamLSL
import time

stream_name = "WS-default"  # Change to your stream name
stream = StreamLSL(bufsize=10, name=stream_name).connect()
stream.filter(l_freq=1.0, h_freq=40.0)
stream.notch_filter(freqs=60.0)

# Process filtered data here
while True:
    data, ts = stream.get_data(winsize=1.0)
    time.sleep(0.1)
Script 2 — StreamViewer for raw stream monitoring (run separately)#
from mne_lsl.stream_viewer import StreamViewer

stream_name = "WS-default"  # Change to your stream name
viewer = StreamViewer(stream_name=stream_name)
viewer.start()  # Blocking — exits the process when window closes

Next Steps#

Now that you can visualize your data in real-time:

  1. Apply filtering - Clean signals and remove noise

  2. Create epochs - Extract event-related segments

  3. Build BCIs - Develop neurofeedback and classification systems

  4. MNE-Python analysis - Offline analysis and advanced processing

Troubleshooting#

Window won’t open: See Installation and ensure dependencies are met.

Choppy display: Reduce window duration or number of channels.

No signal: Check stream is running and accessible.

Resources#