Using the LSL App

NeuroPawn-LSL is a small, dependency-light Python application with two parts:

File	Purpose
knight_lsl_gui.py	Connects to the board over serial, sends channel/RLD/gain commands, parses packets, and publishes an LSL stream.
knight_lsl_receiver.py	Resolves the stream and shows a live scrolling plot of every channel.

No coding is required to use either script — both are self-contained GUIs.

Install

Clone the repo and install its dependencies (pyserial, numpy, scipy, pylsl, and matplotlib):

git clone https://github.com/NeuroPawn/NeuroPawn-LSL.git
cd NeuroPawn-LSL
pip install -r requirements.txt

Run the Streamer

python knight_lsl_gui.py

In the streamer window:

1. Connection — pick the board’s serial port and a gain (default 12), then click Connect.
2. Channels / RLD — check the channels you want active and, optionally, add them to the RLD network.
3. Filters — optional software notch (50/60 Hz) and 1–40 Hz bandpass, applied before the data reaches LSL.
4. Start Stream — queues the chon_/rldadd_ commands for your selections, then listens to the live packets to auto-detect whether it’s talking to a standard board (8 channels) or an IMU board (8 EEG + 9 motion channels).

Once detection succeeds, the app opens an LSL outlet named NeuroPawnKnight at 125 Hz. Channel labels (EXG1–EXG8, plus AccelX/Y/Z, GyroX/Y/Z, MagX/Y/Z on IMU boards) are attached as stream metadata, so downstream tools don’t need to guess the layout.

Tip

Detection only runs once channels are actually producing packets, so make sure at least one channel is on before pressing Start Stream. If detection fails, the log will tell you to enable a channel and try again.

Run the Receiver

In a second terminal:

python knight_lsl_receiver.py

The receiver auto-discovers the NeuroPawnKnight stream (or lets you pick any other LSL stream on the network), connects, and plots every channel in a scrolling window. EEG channels are shown on a fixed µV scale that you can adjust; IMU channels autoscale to their own range.