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Appl. Sci. 2018, 8(1), 149; doi:10.3390/app8010149

Real-Time Reduction of Task-Related Scalp-Hemodynamics Artifact in Functional Near-Infrared Spectroscopy with Sliding-Window Analysis

Department of Electrical, Electronics and Information Engineering, Nagaoka University of Technology, 1603-1, Kamitomioka Nagaoka, Niigata 940-2188, Japan
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Received: 19 December 2017 / Revised: 10 January 2018 / Accepted: 18 January 2018 / Published: 22 January 2018
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Abstract

Functional near-infrared spectroscopy (fNIRS) is an effective non-invasive neuroimaging technique for measuring hemoglobin concentration in the cerebral cortex. Owing to the nature of fNIRS measurement principles, measured signals can be contaminated with task-related scalp blood flow (SBF), which is distributed over the whole head and masks true brain activity. Aiming for fNIRS-based real-time application, we proposed a real-time task-related SBF artifact reduction method. Using a principal component analysis, we estimated a global temporal pattern of SBF from few short-channels, then we applied a general linear model for removing it from long-channels that were possibly contaminated by SBF. Sliding-window analysis was applied for both signal steps for real-time processing. To assess the performance, a semi-real simulation was executed with measured short-channel signals in a motor-task experiment. Compared with conventional techniques with no elements of SBF, the proposed method showed significantly higher estimation performance for true brain activation under a task-related SBF artifact environment. View Full-Text
Keywords: functional near-infrared spectroscopy; sliding-window analysis; general-linear model; scalp-hemodynamics artifact; real-time signal processing functional near-infrared spectroscopy; sliding-window analysis; general-linear model; scalp-hemodynamics artifact; real-time signal processing
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Oda, Y.; Sato, T.; Nambu, I.; Wada, Y. Real-Time Reduction of Task-Related Scalp-Hemodynamics Artifact in Functional Near-Infrared Spectroscopy with Sliding-Window Analysis. Appl. Sci. 2018, 8, 149.

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