amica 0.0.1

Native Python implementation of AMICA for MNE-Python and scientific EEG workflows.

amica

amica is a native Python implementation of AMICA (Adaptive Mixture Independent Component Analysis), one of the highest-performing ICA algorithms for EEG source separation.

Originally distributed as a closed-source Fortran executable from UCSD, this implementation of amica provides an open, extensible implementation with optional JAX acceleration, seamless MNE-Python integration, and a modern Python API for reproducible neuroimaging workflows.

Status: amica is under active development and validation. The implementation already achieves numerical agreement with the original MATLAB AMICA across a broad range of configurations, while documentation and benchmarking continue to expand.

---

Highlights

• Native Python implementation of the AMICA algorithm
• Numerical agreement with the original MATLAB AMICA implementation
• Optional JAX backend for CPU and GPU acceleration
• Native integration with MNE-Python
• Support for multi-model AMICA
• Modern scientific Python API
• Extensive testing and continuous integration
• Fully open source (BSD-3-Clause)

---

Installation

Using pip

git clone https://github.com/snesmaeili/amica.git
cd amica
pip install -e .

Install optional dependencies:

pip install -e ".[jax]"
pip install -e ".[mne]"
pip install -e ".[dev]"
pip install -e ".[all]"

Using uv

git clone https://github.com/snesmaeili/amica.git
cd amica

uv venv
source .venv/bin/activate

uv pip install -e .

or

uv pip install -e ".[all]"

---

Quick Start

from amica import Amica, AmicaConfig

config = AmicaConfig(
    max_iter=2000,
    num_mix_comps=3,
)

model = Amica(config, random_state=42)

result = model.fit(data)

sources = model.transform(data)

For MNE-Python:

from amica import fit_ica

ica = fit_ica(raw)

ica.plot_components()
ica.apply(raw)

---

Examples

Example scripts are available in the examples/ directory, including:

• MNE-Python integration
• Native AMICA API
• JAX acceleration
• Multi-model AMICA
• HPC / SLURM execution

---

Documentation

Full documentation, API reference, validation experiments, and tutorials are available at

https://snesmaeili.github.io/amica/

---

Validation

amica has been validated against the original MATLAB AMICA implementation and achieves numerical agreement across a wide range of configurations.

The documentation contains:

• validation experiments
• numerical parity analyses
• performance benchmarks
• reproducibility instructions

---

Contributing

Contributions are welcome!

Please read CONTRIBUTING.md before opening a pull request.

---

Citation

If amica contributes to your research, please cite the original AMICA publications.

Citation metadata is available in CITATION.cff.

---

License

amica is distributed under the terms of the BSD 3-Clause License.