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@techreport{palmer2012amica,
title = {{AMICA}: An adaptive mixture of independent component analyzers with shared components},
author = {Palmer, Jason A. and Kreutz-Delgado, Kenneth and Makeig, Scott},
year = {2012},
institution = {Swartz Center for Computational Neuroscience, University of California San Diego},
url = {https://sccn.ucsd.edu/~jason/amica_a.pdf}
}
@article{delorme2012independent,
title = {Independent {EEG} sources are dipolar},
author = {Delorme, Arnaud and Palmer, Jason and Onton, Julie and Oostenveld, Robert and Makeig, Scott},
journal = {PLoS ONE},
volume = {7},
number = {2},
pages = {e30135},
year = {2012},
publisher = {Public Library of Science},
doi = {10.1371/journal.pone.0030135}
}
@inproceedings{makeig1995independent,
title = {Independent component analysis of electroencephalographic data},
author = {Makeig, Scott and Bell, Anthony J. and Jung, Tzyy-Ping and Sejnowski, Terrence J.},
booktitle = {Advances in Neural Information Processing Systems},
volume = {8},
pages = {145--151},
year = {1995}
}
@inproceedings{vigario1997independent,
title = {Independent component analysis for identification of artifacts in magnetoencephalographic recordings},
author = {Vig{\'a}rio, Ricardo and Jousm{\"a}ki, Veikko and H{\"a}m{\"a}l{\"a}inen, Matti and Hari, Riitta and Oja, Erkki},
booktitle = {Advances in Neural Information Processing Systems},
volume = {10},
pages = {229--235},
year = {1997}
}
@incollection{iversen2019megeeg,
title = {{MEG/EEG} data analysis using {EEGLAB}},
author = {Iversen, John R. and Makeig, Scott},
booktitle = {Magnetoencephalography: From Signals to Dynamic Cortical Networks},
pages = {391--406},
year = {2019},
publisher = {Springer International Publishing},
address = {Cham},
doi = {10.1007/978-3-319-62657-4_8-1}
}
@article{delorme2004eeglab,
title = {{EEGLAB}: an open source toolbox for analysis of single-trial {EEG} dynamics including independent component analysis},
author = {Delorme, Arnaud and Makeig, Scott},
journal = {Journal of Neuroscience Methods},
volume = {134},
number = {1},
pages = {9--21},
year = {2004},
publisher = {Elsevier},
doi = {10.1016/j.jneumeth.2003.10.009}
}
@article{bell1995information,
title = {An information-maximization approach to blind separation and blind deconvolution},
author = {Bell, Anthony J. and Sejnowski, Terrence J.},
journal = {Neural Computation},
volume = {7},
number = {6},
pages = {1129--1159},
year = {1995},
publisher = {MIT Press},
doi = {10.1162/neco.1995.7.6.1129}
}
@article{lee1999independent,
title = {Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources},
author = {Lee, Te-Won and Girolami, Mark and Sejnowski, Terrence J.},
journal = {Neural Computation},
volume = {11},
number = {2},
pages = {417--441},
year = {1999},
publisher = {MIT Press},
doi = {10.1162/089976699300016719}
}
@article{hyvarinen2000independent,
title = {Independent component analysis: algorithms and applications},
author = {Hyv{\"a}rinen, Aapo and Oja, Erkki},
journal = {Neural Networks},
volume = {13},
number = {4-5},
pages = {411--430},
year = {2000},
publisher = {Elsevier},
doi = {10.1016/S0893-6080(00)00026-5}
}
@article{ablin2018faster,
title = {Faster independent component analysis by preconditioning with Hessian approximations},
author = {Ablin, Pierre and Cardoso, Jean-Fran{\c{c}}ois and Gramfort, Alexandre},
journal = {IEEE Transactions on Signal Processing},
volume = {66},
number = {15},
pages = {4040--4053},
year = {2018},
publisher = {IEEE},
doi = {10.1109/TSP.2018.2844203}
}
@article{gramfort2013meg,
title = {{MEG} and {EEG} data analysis with {MNE-Python}},
author = {Gramfort, Alexandre and Luessi, Martin and Larson, Eric and Engemann, Denis A. and Strohmeier, Daniel and Brodbeck, Christian and Goj, Roman and Jas, Mainak and Brooks, Teon and Parkkonen, Lauri and H{\"a}m{\"a}l{\"a}inen, Matti},
journal = {Frontiers in Neuroscience},
volume = {7},
pages = {267},
year = {2013},
publisher = {Frontiers},
doi = {10.3389/fnins.2013.00267}
}
@inproceedings{paszke2019pytorch,
title = {{PyTorch}: An imperative style, high-performance deep learning library},
author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and others},
booktitle = {Advances in Neural Information Processing Systems},
volume = {32},
pages = {8024--8035},
year = {2019},
note = {arXiv:1912.01703}
}
@article{harris2020array,
title = {Array programming with {NumPy}},
author = {Harris, Charles R. and Millman, K. Jarrod and van der Walt, St{\'e}fan J. and Gommers, Ralf and Virtanen, Pauli and Cournapeau, David and Wieser, Eric and Taylor, Julian and Berg, Sebastian and Smith, Nathaniel J. and others},
journal = {Nature},
volume = {585},
number = {7825},
pages = {357--362},
year = {2020},
publisher = {Nature Publishing Group},
doi = {10.1038/s41586-020-2649-2}
}
@article{virtanen2020scipy,
title = {{SciPy} 1.0: fundamental algorithms for scientific computing in Python},
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and others},
journal = {Nature Methods},
volume = {17},
number = {3},
pages = {261--272},
year = {2020},
publisher = {Nature Publishing Group},
doi = {10.1038/s41592-019-0686-2}
}
@article{amari1998natural,
title = {Natural gradient works efficiently in learning},
author = {Amari, Shun-Ichi},
journal = {Neural Computation},
volume = {10},
number = {2},
pages = {251--276},
year = {1998},
publisher = {MIT Press},
doi = {10.1162/089976698300017746}
}
@inproceedings{amari1996new,
title = {A new learning algorithm for blind signal separation},
author = {Amari, Shun-Ichi and Cichocki, Andrzej and Yang, Howard H},
booktitle = {Advances in Neural Information Processing Systems},
volume = {8},
pages = {757--763},
year = {1996}
}
@article{delorme2022nemar,
title = {{NEMAR}: An open access data, tools and compute resource operating on neuroelectromagnetic data},
author = {Delorme, Arnaud and Truong, Dung and Youn, Choonhan and Sivagnanam, Subhashini and Stirm, Kenneth and Yoshimoto, Kenneth and Poldrack, Russell A. and Majumdar, Amit and Makeig, Scott},
journal = {Database},
volume = {2022},
pages = {baac096},
year = {2022},
doi = {10.1093/database/baac096}
}
@inproceedings{palmer2006super,
title = {Super-{G}aussian mixture source model for {ICA}},
author = {Palmer, Jason A. and Kreutz-Delgado, Kenneth and Makeig, Scott},
booktitle = {Independent Component Analysis and Blind Signal Separation: Proceedings of the 6th International Conference (ICA 2006)},
series = {Lecture Notes in Computer Science},
pages = {854--861},
year = {2006},
publisher = {Springer Berlin Heidelberg},
doi = {10.1007/11679363_106}
}
@inproceedings{palmer2007modeling,
title = {Modeling and estimation of dependent subspaces with non-radially symmetric and skewed densities},
author = {Palmer, Jason A. and Kreutz-Delgado, Ken and Rao, Bhaskar D. and Makeig, Scott},
booktitle = {Independent Component Analysis and Signal Separation: Proceedings of the 7th International Conference (ICA 2007)},
editor = {Davies, Mike E. and James, Christopher J. and Abdallah, Samer A. and Plumbley, Mark D.},
series = {Lecture Notes in Computer Science},
pages = {97--104},
year = {2007},
publisher = {Springer Berlin Heidelberg},
doi = {10.1007/978-3-540-74494-8_13}
}
@inproceedings{palmer2008newton,
title = {Newton method for the {ICA} mixture model},
author = {Palmer, Jason A. and Kreutz-Delgado, Kenneth and Rao, Bhaskar D. and Makeig, Scott},
booktitle = {2008 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {1805--1808},
year = {2008},
publisher = {IEEE},
doi = {10.1109/ICASSP.2008.4517982}
}
@article{piontonachini2019iclabel,
title = {{ICLabel}: An automated electroencephalographic independent component classifier, dataset, and website},
author = {Pion-Tonachini, Luca and Kreutz-Delgado, Kenneth and Makeig, Scott},
journal = {NeuroImage},
volume = {198},
pages = {181--197},
year = {2019},
publisher = {Elsevier},
doi = {10.1016/j.neuroimage.2019.05.026}
}
@misc{huberty2025amicapython,
title = {amica-python: Python implementation of Adaptive Mixture {ICA}},
author = {Huberty, Scott},
year = {2025},
howpublished = {\url{https://github.com/scott-huberty/amica-python}},
note = {Accessed 2026-08-09}
}
@misc{esmaeili2025amica,
title = {amica: Native Python implementation of {AMICA} for {MNE-Python} and scientific {EEG} workflows},
author = {Esmaeili, Sina},
year = {2025},
howpublished = {\url{https://github.com/snesmaeili/amica}},
note = {Accessed 2026-07-11}
}
@misc{herforth2026pyamica,
title = {pyamica: {PyTorch} {AMICA}, Adaptive Mixture Independent Component Analysis},
author = {Herforth, Johannes},
year = {2026},
howpublished = {\url{https://github.com/DerAndereJohannes/pyamica}},
note = {Accessed 2026-07-11}
}
@misc{mlx2023,
title = {{MLX}: Efficient and flexible machine learning on Apple silicon},
author = {Hannun, Awni and Digani, Jagrit and Katharopoulos, Angelos and Collobert, Ronan},
year = {2023},
note = {Version 0.x, accessed 2026},
howpublished = {\url{https://github.com/ml-explore/mlx}}
}
@inproceedings{frank2023optimal,
title = {An exploration of optimal parameters for efficient blind source separation of {EEG} recordings using {AMICA}},
author = {Frank, Gwenevere and Shirazi, Seyed Yahya and Palmer, Jason and Cauwenberghs, Gert and Makeig, Scott and Delorme, Arnaud},
booktitle = {2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering (BIBE)},
pages = {205--210},
year = {2023},
publisher = {IEEE},
doi = {10.1109/BIBE60311.2023.00040}
}
@inproceedings{frank2025sufficient,
title = {Quantitative exploration of sufficient data quantities for {EEG} decomposition using {AMICA}},
author = {Frank, Gwenevere and Shirazi, Seyed Yahya and Palmer, Jason and Cauwenberghs, Gert and Makeig, Scott and Delorme, Arnaud},
booktitle = {2025 12th International IEEE/EMBS Conference on Neural Engineering (NER)},
pages = {532--536},
year = {2025},
publisher = {IEEE},
doi = {10.1109/NER61569.2025.11588993}
}