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Updated: Mar 20, 2026
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
Published on: October 24, 2012
Dynamic filtering improves attentional state prediction with fNIRS
Angela R Harrivel1, Daniel H Weissman2, Douglas C Noll3
1Crew Systems & Aviation Operations Branch, NASA Langley Research Center, Hampton, VA, 23681, USA; Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA; Functional MRI Laboratory, University of Michigan, Ann Arbor, MI, 48109, USA.
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%).
