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Dynamic Filtering Improves Attentional State Prediction with fNIRSBrain 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%).
Document ID
Document Type
External Source(s)
Harrivel, Angela R.
(NASA Langley Research Center Hampton, VA, United States)
Weissman, Daniel H.
(Michigan Univ. Ann Arbor, MI, United States)
Noll, Douglas C.
(Michigan Univ. Ann Arbor, MI, United States)
Huppert, Theodore
(Pittsburgh Univ. Bradford, PA, United States)
Peltier, Scott J.
(Michigan Univ. Ann Arbor, MI, United States)
Date Acquired
July 25, 2017
Publication Date
February 23, 2016
Publication Information
Publication: Biomedical Optics Express
Volume: 7
Issue: 3
ISSN: 2156-7085
e-ISSN: 2156-7085
Subject Category
Behavioral Sciences
Report/Patent Number
Funding Number(s)
WBS: WBS 284848.02.50.07
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