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Identifying reliable independent components via split-half comparisons

Independent component analysis (ICA) is a family of unsupervised learning algorithms that have proven useful for the analysis of the electroencephalogram (EEG) and magnetoencephalogram (MEG). ICA decomposes an EEG/MEG data set into a basis of maximally temporally independent components (ICs) that ar...

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Bibliografski detalji
Glavni autori: Groppe, David M., Makeig, Scott, Kutas, Marta
Format: Artigo
Jezik:Inglês
Izdano: 2008
Teme:
Online pristup:https://ncbi.nlm.nih.gov/pmc/articles/PMC3062525/
https://ncbi.nlm.nih.gov/pubmed/19162199
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.neuroimage.2008.12.038
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