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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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| Auteurs principaux: | , , |
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| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
2008
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| Sujets: | |
| Accès en ligne: | 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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