We introduce a new robust independent component analysis (ICA) toolbox for neuroinformatics, called "Arabica". The toolbox is designed to be modular and extendable also to other types of data. The robust ICA is the result of extensive research on reliable application of ICA to real-world measurements. The approach itself is based on sampling component estimates from multiple runs of ICA using bootstrapping. The toolbox is fully integrated to a recently developed processing pipeline environment, capable of running on a single machine or in a cluster of servers. Additionally, the toolbox works as a standalone package in Matlab, when the full pipeline is not required. The toolbox is aimed at being useful for both machine learning and neuroinformatics researchers.

@inproceedings{
    JYlipaavalniemi-2009-ICA_whyj,
    author = "Ylipaavalniemi, Jarkko and Soppela, Jyri",
    title = "{A}rabica: {R}obust {ICA} in a {P}ipeline",
    url = "http://dx.doi.org/10.1007/978-3-642-00599-2_48",
    booktitle = "8th International Conference on Independent Component Analysis and Signal Separation (ICA 2009)",
    year = "2009",
    month = "March",
    responsibleauthor = "Ylipaavalniemi, Jarkko",
    keywords = "toolbox, pipeline, bootstrapping, independent component analysis, functional magnetic resonance imaging",
    flags = "AIRC",
    pages = "379--386",
    address = "Paraty, Brazil",
    internalpdf = "JYlipaavalniemi-2009-ICA.pdf",
    kay = "NA",
    impactfactor = "D3",
    abstract = {We introduce a new robust independent component analysis (ICA) toolbox for neuroinformatics, called "Arabica". The toolbox is designed to be modular and extendable also to other types of data. The robust ICA is the result of extensive research on reliable application of ICA to real-world measurements. The approach itself is based on sampling component estimates from multiple runs of ICA using bootstrapping. The toolbox is fully integrated to a recently developed processing pipeline environment, capable of running on a single machine or in a cluster of servers. Additionally, the toolbox works as a standalone package in Matlab, when the full pipeline is not required. The toolbox is aimed at being useful for both machine learning and neuroinformatics researchers.}
}

AUTHORS:

ABSTRACT:

URL:
http://dx.doi.org/10.1007/978-3-642-00599-2_48

INTERNAL PDF:
http://research.ics.aalto.fi/publications/bibdb2012/internal_pdfs/JYlipaavalniemi-2009-ICA.pdf

AUTHORS:

ABSTRACT:

URL: http://dx.doi.org/10.1007/978-3-642-00599-2_48

INTERNAL PDF: http://research.ics.aalto.fi/publications/bibdb2012/internal_pdfs/JYlipaavalniemi-2009-ICA.pdf

URL:
http://dx.doi.org/10.1007/978-3-642-00599-2_48

INTERNAL PDF:
http://research.ics.aalto.fi/publications/bibdb2012/internal_pdfs/JYlipaavalniemi-2009-ICA.pdf