biogui

Modular PySide6 GUI for acquiring and visualizing bio-signals from different sources.

Usage

Environment setup

The code is compatible with Python 3.7+. To create and activate the Python environment, run the following commands:

python -m venv <ENV_NAME>
source <ENV_NAME>/bin/activate

Then, from within the virtual environment, the required packages can be installed with the following command:

pip install -r requirements.txt

Interface with board

To enable the communication between the GUI and a board, one must provide a Python file with the following specifications:

• packetSize: integer representing the number of bytes to be read;
• startSeq: sequence of commands to start the board, expressed as a list of bytes;
• stopSeq: sequence of commands to stop the board, expressed as a list of bytes;
• fs: list of sampling frequencies, one for each signal to read from the board;
• nCh: list of numbers of channels, one for each signal to read from the board;
• SigsPacket: named tuple containing one field for each signal to read from the board;
• decodeFn: function that decodes each packet of byte read from the board into the specified signals.

Some examples of interface files are provided in the interfaces folder.

Execution

Run the script main.py, which launches the main window.

Authors

This work was realized mainly at the Energy-Efficient Embedded Systems Laboratory (EEES Lab) of University of Bologna (Italy) by:

• Mattia Orlandi
• Pierangelo Maria Rapa

Citation

If you would like to reference the project, please cite the following paper:

@ARTICLE{10552147,
  author={Orlandi, Mattia and Rapa, Pierangelo Maria and Zanghieri, Marcello and Frey, Sebastian and Kartsch, Victor and Benini, Luca and Benatti, Simone},
  journal={IEEE Transactions on Biomedical Circuits and Systems}, 
  title={Real-Time Motor Unit Tracking From sEMG Signals With Adaptive ICA on a Parallel Ultra-Low Power Processor}, 
  year={2024},
  volume={18},
  number={4},
  pages={771-782},
  keywords={Electrodes;Real-time systems;Muscles;Motors;Electromyography;Circuits and systems;Graphical user interfaces;Blind source separation;human-machine interfaces;independent component analysis;low-power;machine learning;on-device learning;online learning;PULP;surface EMG},
  doi={10.1109/TBCAS.2024.3410840}}

License

All files are released under the Apache-2.0 license (see LICENSE).