发表机构
University of Southern California; University of South Carolina（南加州大学; 南卡罗来纳大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对AMICA算法依赖单一Fortran实现、限制其在非MATLAB生态系统中应用的问题，提出AMICA-Python这一Python实现，通过符合scikit-learn的API集成，引入安德森加速方案，经测试其数值精度高、运行时具竞争力。
AI 中文摘要
自适应混合独立成分分析（AMICA）在脑电图研究中广泛应用，长期以来在盲源分离方面经验性能强劲。但其实际应用依赖于通过MATLAB的EEGLAB工具箱访问的单一Fortran实现，限制了非MATLAB生态系统中分析管道的可用性。本文提出AMICA-Python，即AMICA算法的Python实现，具有符合scikit-learn的API，便于与现有科学Python管道集成。实现过程紧密遵循参考算法，采用现代软件工程实践和Python用户熟悉的接口。还引入了可选的安德森加速方案，可大幅减少该相对缓慢算法的收敛时间。在14个开放脑电图记录上对AMICA-Python与参考Fortran实现进行基准测试，结果显示二者数值一致性高，运行时也具竞争力。AMICA-Python以高数值精度重现参考实现，运行时具有竞争力，同时通过更易访问和可扩展的Python接口使AMICA可用。
英文摘要
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.
