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A method to compare the discriminatory power of data-driven methods: Application to ICA and IVA.
Levin-Schwartz Y, Calhoun VD, Adalı T. Levin-Schwartz Y, et al. J Neurosci Methods. 2019 Jan 1;311:267-276. doi: 10.1016/j.jneumeth.2018.10.008. Epub 2018 Oct 30. J Neurosci Methods. 2019. PMID: 30389489 Free PMC article.
BACKGROUND: The widespread application of data-driven factorization-based methods, such as independent component analysis (ICA), to functional magnetic resonance imaging data facilitates the study of neural function and how it is disrupte …
BACKGROUND: The widespread application of data-driven factorization-based methods, such as independent component …
Disjoint subspaces for common and distinct component analysis: Application to the fusion of multi-task FMRI data.
Akhonda MABS, Gabrielson B, Bhinge S, Calhoun VD, Adali T. Akhonda MABS, et al. J Neurosci Methods. 2021 Jul 1;358:109214. doi: 10.1016/j.jneumeth.2021.109214. Epub 2021 May 3. J Neurosci Methods. 2021. PMID: 33957159 Free PMC article.
BACKGROUND: Data-driven methods such as independent component analysis (ICA) makes very few assumptions on the data and the relationships of multiple datasets, and hence, are attractive for the fusion of medical imaging data. ...COMPARISO …
BACKGROUND: Data-driven methods such as independent component analysis (ICA) makes very few assumptions on the …