Xu Cui1, Joseph M Baker1, Ning Liu1, Allan L Reiss1,2. 1. Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine. 2. Department of Radiology, Stanford University School of Medicine.
Abstract
BACKGROUND: Powerful computing capabilities in small, easy to use hand-held devices have made smart technologies such as smartphones and tablets ubiquitous in today's society. The capabilities of these devices provide scientists with many tools that can be used to improve the scientific method. METHOD: Here, we demonstrate how smartphones may be used to quantify the sensitivity of functional near-infrared spectroscopy (fNIRS) signal to head motion. By attaching a smartphone to participants' heads during the fNIRS scan, we were able to capture data describing the degree of head motion. RESULTS: Our results demonstrate that data recorded from an off-the-shelf smartphone accelerometer may be used to identify correlations between head-movement and fNIRS signal change. Furthermore, our results identify correlations between the magnitudes of head-movement and signal artifact, as well as a relationship between the direction of head movement and the location of the resulting signal noise. CONCLUSIONS: These data provide a valuable proof-of-concept for the use of off-the-shelf smart technologies in neuroimaging applications.
BACKGROUND: Powerful computing capabilities in small, easy to use hand-held devices have made smart technologies such as smartphones and tablets ubiquitous in today's society. The capabilities of these devices provide scientists with many tools that can be used to improve the scientific method. METHOD: Here, we demonstrate how smartphones may be used to quantify the sensitivity of functional near-infrared spectroscopy (fNIRS) signal to head motion. By attaching a smartphone to participants' heads during the fNIRS scan, we were able to capture data describing the degree of head motion. RESULTS: Our results demonstrate that data recorded from an off-the-shelf smartphone accelerometer may be used to identify correlations between head-movement and fNIRS signal change. Furthermore, our results identify correlations between the magnitudes of head-movement and signal artifact, as well as a relationship between the direction of head movement and the location of the resulting signal noise. CONCLUSIONS: These data provide a valuable proof-of-concept for the use of off-the-shelf smart technologies in neuroimaging applications.
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