swarm-contrastive-decomposition 0.1.0

Decomposition of Neurophysiological Time Series Signals with a Particle Swarm Optimised Independence Estimator

Swarm-Contrastive Decomposition 🧠

Overview 📝

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 🛠️

Prerequisites

• Python 3.10+
• PyTorch with CUDA support (recommended)

Option 1: Install as a Package (Recommended)

# Clone the repository
git clone https://github.com/AgneGris/swarm-contrastive-decomposition
cd swarm-contrastive-decomposition

# Install the package
pip install -e .

Option 2: Using Conda Environment

# Clone the repository
git clone https://github.com/AgneGris/swarm-contrastive-decomposition
cd swarm-contrastive-decomposition

# Create conda environment
conda env create -f decomposition.yml
conda activate decomposition

# Install the package
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("data/input/emg.npy")

# 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(
    "data/input/emg.npy",
    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("data/input/emg.npy", 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 (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:

Config Name	Use Case	Sampling Rate	Description
default	General purpose	10240 Hz	Balanced settings for most EMG data
surface	Surface EMG	10240 Hz	Optimized for surface recordings
intramuscular	Intramuscular EMG	10240 Hz	Higher iterations for fine-wire recordings

Configuration Parameters

Parameter	Description	Default
device	"cuda" for GPU or "cpu"	"cuda"
acceptance_silhouette	Quality threshold for source acceptance	0.85
extension_factor	Typically 1000 / num_channels. Higher values may improve results	25
low_pass_cutoff	Low-pass filter cutoff frequency (Hz)	4400
high_pass_cutoff	High-pass filter cutoff frequency (Hz)	10
sampling_frequency	Sampling frequency of your signal (Hz)	10240
start_time	Start time for signal trimming (s). Use 0 for beginning	0
end_time	End time for signal trimming (s). Use -1 for entire signal	-1
max_iterations	Maximum decomposition iterations	200
peel_off_window_size_ms	Window size for spike-triggered average (ms)	20
output_final_source_plot	Generate plot of final sources	false
use_coeff_var_fitness	Use coefficient of variation fitness. true for EMG, false for intracortical	true
remove_bad_fr	Filter sources with firing rates < 2 Hz or > 100 Hz	true
clamp_percentile	Percentile for amplitude clamping	0.999

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")

Test Data 🧪

The repository includes test data to verify your installation:

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

import scd

# Run with test data
dictionary, timestamps = scd.train(
    "data/input/emg.npy",
    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 CC BY-NC 4.0 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={2024},
  volume={},
  number={},
  pages={1-11},
  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}
}

Contact

For questions or inquiries:

Agnese Grison
📧 [e-mail]