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This paper introduces AMICA-Python, a Python implementation of the Adaptive Mixture Independent Component Analysis (AMICA) algorithm, aimed at enhancing accessibility for EEG research by integrating with existing scientific Python pipelines. The authors benchmarked AMICA-Python against the traditional Fortran implementation, demonstrating that it achieves comparable numerical precision with a median normalized log-likelihood of 11.572 and a negligible relative absolute difference. Notably, AMICA-Python is not only 17.7% faster than the Fortran version, but the Anderson-accelerated variant further improves performance by 34.1%, making it a robust tool for blind source separation in EEG analysis.
AMICA-Python achieves the same high precision as its Fortran predecessor while being significantly faster and more accessible for Python users.
Adaptive Mixture Independent Component Analysis (AMICA) is widely used in EEG research and has long been associated with strong empirical performance for blind source separation. Despite its impact, practical use has historically depended on a single Fortran implementation, accessed via the EEGLAB toolbox for MATLAB, limiting its accessibility for analytical pipelines not designed within the MATLAB ecosystem. Here we present AMICA-Python, a Python implementation of the AMICA algorithm, with a scikit-learn-conformant API designed for integration with existing scientific Python pipelines. The implementation follows the reference algorithm closely while adopting modern software engineering practices and an interface familiar to Python users. Additionally, we introduce an optional Anderson acceleration scheme that can dramatically reduce the time to convergence for this relatively slow algorithm. To evaluate numerical agreement and practical performance, we benchmarked AMICA-Python against the reference Fortran implementation on 14 open EEG recordings. After averaging 3 runs of each implementation on all 14 recordings, AMICA-Python closely matched the reference, with a median final normalized log-likelihoods of 11.572 for both the Fortran and Python implementations, and a negligible median relative absolute difference of only $1.07\times10^{-8}$ when normalized by the absolute Fortran value. Runtime was also competitive. Relative to the reference implementation, AMICA-Python was 17.7\% faster, while the Anderson-accelerated variant was 34.1\% faster. AMICA-Python reproduces the reference implementation to high numerical precision with competitive runtime, while making AMICA available through a more accessible and extensible Python interface.