Work

Dynamic filtering improves attentional state prediction with fNIRS

Year: 2016

Type: article

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%).

Source: Biomedical Optics Express

DOI: 10.1364/boe.7.000979

Authors: Angela R. Harrivel, Daniel H. Weissman, Douglas C. Noll, Theodore Huppert, Scott J. Peltier

Institutions: Langley Research Center, University of Michigan, University of Pittsburgh

Language: English

Topic: Optical Imaging and Spectroscopy Techniques

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,913