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Proceedings Paper

Method for leveling the signal-to-noise ratio in multichannel functional near-infrared spectroscopy
Author(s): Toru Yamada; Shinji Umeyama; Atsushi Kamoshida
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Paper Abstract

The difference in signal-to-noise ratio (SNR) within functional near-infrared spectroscopic (fNIRS)-measurement channels makes it difficult to compare the significance of the signal amplitude in an individual channel against the baseline or against signals in other channels. The difference in SNR mainly originates from the difference in light loss due to the hair coverage or the optode–scalp contact. During the initial calibration of most commercial fNIRS equipment, the detected signals in different channels are differently amplified such that the system effectively utilizes a dynamic range for measurement. While different amplification rates among the channels realize almost equal signal intensities, they also differently amplify the detection noises. This results in different apparent noises in the fNIRS signals between channels. In order to level the SNRs in all the measurement channels, the system needs to equalize the light intensities received by the detectors instead of changing the signal amplification rates. To realize this novel procedure for leveling the SNR among the channels, we developed an fNIRS system equipped with an optical attenuator at each source and detector. A systematic procedure for modulating the attenuators to level SNR over all channels was mathematically formulated, and the procedure was examined using an optical phantom with a surface covered with air.

Paper Details

Date Published: 8 February 2017
PDF: 7 pages
Proc. SPIE 10051, Neural Imaging and Sensing, 1005118 (8 February 2017); doi: 10.1117/12.2250239
Show Author Affiliations
Toru Yamada, National Institute of Advanced Industrial Science and Technology (Japan)
Shinji Umeyama, National Institute of Advanced Industrial Science and Technology (Japan)
Atsushi Kamoshida, National Instruments Japan Corp. (Japan)

Published in SPIE Proceedings Vol. 10051:
Neural Imaging and Sensing
Qingming Luo; Jun Ding, Editor(s)

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