Swarm-Contrastive Decomposition 🧠

A Python package for decomposition of neurophysiological time series signals using a Particle Swarm Optimised Independence Estimator for Blind Source Separation.

Table of Contents 📚

• Installation
• Quick Start
• Usage
• Configuration
• Test Data
• Contributing
• License
• Citation
• Contact

Installation 🛠️

From PyPI (Recommended)

pip install swarm-contrastive-decomposition

From GitHub (Latest Development Version)

pip install git+https://github.com/AgneGris/swarm-contrastive-decomposition.git

From Source

git clone https://github.com/AgneGris/swarm-contrastive-decomposition
cd swarm-contrastive-decomposition
pip install -e .

Verify Installation

python -c "import scd; print(f'SCD version: {scd.__version__}')"

Quick Start 🚀

import scd

# Train with default configuration
dictionary, timestamps = scd.train("path/to/your/data.mat")

# Save results
scd.save_results("data/output/emg.pkl", dictionary)

Usage

Basic Usage

import scd

# Use a predefined configuration
dictionary, timestamps = scd.train(
    "path/to/your/data.mat",
    config_name="surface"  # or "default", "intramuscular"
)

scd.save_results("output.pkl", dictionary)

With Configuration Overrides

import scd

# Override specific parameters
dictionary, timestamps = scd.train(
    "path/to/your/data.mat",
    config_name="surface",
    max_iterations=100,  # override for quick testing
    output_final_source_plot=True
)

Step-by-Step Control

import scd

# Load configuration
config = scd.load_config("surface")

# Load data
neural_data = scd.load_data("path/to/your/data.mat", device=config.device)

# Preprocess
neural_data = scd.preprocess_data(neural_data, config)

# Train model
dictionary, timestamps = scd.train_model(neural_data, config)

# Save results
scd.save_results("output.pkl", dictionary)

Supported Data Formats

• .mat — MATLAB files, including HDF5-based v7.3 files (specify the variable name with key parameter)
• .npy — NumPy arrays

# For .mat files with custom variable name
dictionary, timestamps = scd.train("data.mat", key="emg_data")

# For .npy files
dictionary, timestamps = scd.train("data.npy")

Data should have shape (time, channels) or (channels, time) — the loader will automatically transpose if needed.

Configuration ⚙️

Configurations are defined in scd/configs.json. Available presets:

Configuration Parameters

Custom Configuration

Add your own configuration to scd/configs.json:

{
    "my_experiment": {
        "device": "cuda",
        "acceptance_silhouette": 0.80,
        "extension_factor": 30,
        "sampling_frequency": 2048,
        ...
    }
}

Then use it:

dictionary, timestamps = scd.train("data.mat", config_name="my_experiment")

Choosing the Extension Factor

K is chosen so that the extended observation matrix has on the order of 1000 rows:

K  ≈  1000 / M          M = kept channels = total channels − bad_channels

This is the default. If extension_factor is absent from your config, SCD derives it from the channel count of the data you pass in and logs the value it used:

INFO - extension_factor not set; using 16 (1000 / 63 kept channels)

Set extension_factor explicitly in your config to override it with any value you like — the derived default applies only when it is unset. All three built-in presets set it explicitly.

To compute the same number yourself:

from scd import recommended_extension_factor

K = recommended_extension_factor(num_channels=64, bad_channels=[56])   # 16

Whatever you set is used as-is: SCD never rejects or adjusts an extension factor. Note that larger K costs time and memory, as the covariance and whitening stages scale roughly with (K · M)² and (K · M)³ respectively, and that whitening runs on CPU.

Changed in 0.2.0

This release contains breaking changes. Configs written for 0.1.x may need editing — Config rejects unknown keys, so a removed parameter left in a config file raises TypeError on load.

No extension factor is rejected any more. Versions 0.1.7 and 0.1.8 validated extension_factor against a temporal-separation bound K ≤ T − L and raised a ValueError when it was exceeded. That bound is uninformative: SCD's assumed MUAP duration (peel_off_window_size_ms, 20 ms) equals the minimum ISI at max_firing_rate_hz = 50, so it evaluates to zero at every sampling rate. In practice it blocked valid configurations, particularly at lower sampling rates such as 2048 Hz, where the standard 1000 / M choice exceeds it.

Changed defaults:

• extension_factor was a fixed 100, which silently produced badly over-extended runs for any config that omitted the key. It is now unset by default and derived as 1000 / kept channels.
• edge_mask_size is now specified in ms as edge_mask_size_ms and scaled by the sampling rate; it was a raw sample count tuned for 10 240 Hz. Set edge_mask_size to pin a sample count instead.
• Rejected channels are replaced with baseline noise rather than zeroed. Zeroing left exactly-zero rows in the extended matrix, making the covariance rank-deficient and letting whitening amplify those directions. Amplitude comes from estimate_baseline_noise, a MAD-based estimate of the noise floor between discharges.

Runs using bad_channels will give different results than 0.1.x. Note also that scd-edition scales its channel fill by the pooled standard deviation of the good channels, which includes MUAP activity; pass noise_std explicitly to replace_bad_channels_with_noise to reproduce its output.

Test Data 🧪

The source repository includes test data to verify a development installation:

• File: data/input/emg.mat
• Type: Surface EMG
• Sampling rate: 10240 Hz
• Configuration: Use "surface" config

import scd

# Run with test data
dictionary, timestamps = scd.train(
    "data/input/emg.mat",
    config_name="surface"
)

print(f"Found {len(dictionary)} motor units")

Contributing 🤝

We welcome contributions! Here's how you can contribute:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/newfeature)
3. Commit your changes (git commit -m 'Add some newfeature')
4. Push to the branch (git push origin feature/newfeature)
5. Open a pull request

License 📜

This project is licensed under the BSD 3-Clause License.

Citation

If you use this code in your research, please cite our paper:

@article{grison2024particle,
  author={Grison, Agnese and Clarke, Alexander Kenneth and Muceli, Silvia and Ibáñez, Jaime and Kundu, Aritra and Farina, Dario},
  journal={IEEE Transactions on Biomedical Engineering}, 
  title={A Particle Swarm Optimised Independence Estimator for Blind Source Separation of Neurophysiological Time Series}, 
  year={2025},
  volume={72},
  number={1},
  pages={227--237},
  doi={10.1109/TBME.2024.3446806},
  keywords={Recording; Time series analysis; Sorting; Vectors; Measurement; Electrodes; Probes; Independent component analysis; particle swarm optimisation; blind source separation; intramuscular electromyography; intracortical recording}
}

@article{grison2025unlocking,
  title={Unlocking the full potential of high-density surface EMG: novel non-invasive high-yield motor unit decomposition},
  author={Grison, Agnese and Mendez Guerra, Irene and Clarke, Alexander Kenneth and Muceli, Silvia and Ib{\'a}{\~n}ez, Jaime and Farina, Dario},
  journal={The Journal of Physiology},
  volume={603},
  number={8},
  pages={2281--2300},
  year={2025},
  publisher={Wiley Online Library}
}

Contact

For questions or inquiries:

Agnese Grison
📧 agnese.grison@outlook.it