This paper presents a dynamic optical phantom for the simulation of metabolic activities in the brain, and a linear equivalent model is built for control voltage versus substance concentration. A solid–solid dynamic optical phantom is realized by using liquid crystal film as a voltage-controlled light intensity regulator on the surface of basic phantom, which uses epoxy resin as matrix material and nanometer carbon powder and titanium dioxide powder as absorption and scattering dopants, respectively. The dynamic phantom could mimic near-infrared spectrum (NIRS) signals with sampling rate up to 10 Hz, and the maximum simulation errors for oxy-hemoglobin and deoxy-hemoglobin concentrations varying in the range of 1 μmol/l are 7.0% and 17.9%, respectively. Compared with similar solid biomimetic phantoms, the adjustable mimic substance concentration range is extended by an order of magnitude, which meets the simulation requirements of most brain NIRS signals.
Functional near-infrared spectroscopy (fNIRS) is considered as a non-invasive and effective brain-computer interface technology. Wearable high-resolution fNIRS requires a largescale LED array, which consumes a lot of power, and shorten the battery life. This paper proposes a spatial adaptive sampling (SAS) method that can take advantage of the spatial sparsity of fNIRS devices and greatly reduce the power consumption while maintaining high image quality. To improve the performance of the proposed SAS technique, a low power binary neural network (BNN) is proposed to accurately predict the current brain task. And the optimal dynamic LED pattern for each brain task is investigated. The proposed SAS technique is validate through an off-line experiment, it can reduce the power consumption of the LED array by 62.5% compared to not using SAS technology while maintaining a PSNR (Peak Signal to Noise Ratio) of 33 dB.Index Terms-Functional near-infrared spectroscopy (fNIRS), power consumption, binary neural network (BNN), spatial adaptive sampling (SAS)
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