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Detecting and Adjusting for Artifacts in fMRI Time Series Data
We present a new method to detect and adjust for noise and artifacts in functional MRI time series data. We note that the assumption of stationary variance, which is central to the theoretical treatment of fMRI time series data, is often violated in practice. Sporadic events such as eye, mouth, or a...
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| Główni autorzy: | , |
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| Format: | Artigo |
| Język: | Inglês |
| Wydane: |
2005
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| Hasła przedmiotowe: | |
| Dostęp online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC1479857/ https://ncbi.nlm.nih.gov/pubmed/15975828 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.neuroimage.2005.04.039 |
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