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- W2283179916 abstract "Brain activity can predict a person's level of engagement in an attentional task. However, estimates of brain activity are often confounded by measurement artifacts and systemic physiological noise. The optimal method for filtering this noise - thereby increasing such state prediction accuracy - remains unclear. To investigate this, we asked study participants to perform an attentional task while we monitored their brain activity with functional near infrared spectroscopy (fNIRS). We observed higher state prediction accuracy when noise in the fNIRS hemoglobin [Hb] signals was filtered with a non-stationary (adaptive) model as compared to static regression (84% ± 6% versus 72% ± 15%)." @default.
- W2283179916 created "2016-06-24" @default.
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- W2283179916 creator A5091219687 @default.
- W2283179916 date "2016-02-23" @default.
- W2283179916 modified "2023-10-17" @default.
- W2283179916 title "Dynamic filtering improves attentional state prediction with fNIRS" @default.
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- W2283179916 doi "https://doi.org/10.1364/boe.7.000979" @default.
- W2283179916 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4866469" @default.
- W2283179916 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/27231602" @default.
- W2283179916 hasPublicationYear "2016" @default.
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