Continuous wave near-infrared spectroscopy (CW-NIRS) can be used to measure cerebral activity because it is noninvasive, simple and portable. However, the performance of the CW-NIRS is distorted by the presence of extracerebral layer. Change of optical parameters in gray matter layer will then be inappropriately converted into the brain activity response. In the current study, a five-layer structure model constitute of scalp, skull, cerebrospinal fluid, gray matter and white matter is adopted and the mixture of the Intralipid, India ink and agar is applied to fabricate human brain tissue. To simulate optical properties in deep layer due to the brain activity, the absorption coefficients of gray matter are increased by 5%, 10%, 15%, 20%, and 25% relative to the baseline. The NIRS measurement system was designed to detect the changes in the absorption coefficients of the gray matter and quantitatively analyse the influence of the extracerebral layers. Monte Carlo technique is performed to compensate partial volume effect (PVE) introduced by the extracerebral layers. The results of the in-vitro experiments show that the measured absorption coefficients are about 9% of the standard value and the relative error is about 91% due to the extracerebral layers. The influence of the extracerebral layers is suppressed by correcting PVE with Monte Carlo simulations and the average relative error is improved to only about 6% for the whole data set. Therefore, the measurement and analysis of the brain activity could be further strengthened if the anatomic structure of the head could be predicted with Monte Carlo method or other technologies.

中文翻译：

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连续波近红外光谱对脑外层对光学性质估计的影响：基于多层脑组织结构和蒙特卡洛模拟的分析。

连续波近红外光谱法（CW-NIRS）可用于测量大脑活动，因为它是无创，简单和便携式的。但是，CW-NIRS的性能会因存在脑外层而扭曲。然后，灰质层中光学参数的变化将被不适当地转换为大脑活动反应。在目前的研究中，采用由头皮，头骨，脑脊液，灰质和白质组成的五层结构模型，并使用脂内，印度墨水和琼脂的混合物制造人脑组织。为了模拟由于大脑活动而引起的深层光学特性，灰质的吸收系数相对于基线增加了5％，10％，15％，20％和25％。NIRS测量系统旨在检测灰质吸收系数的变化并定量分析脑外层的影响。进行蒙特卡洛技术以补偿由脑外层引入的部分体积效应（PVE）。体外实验的结果表明，由于脑外层，测得的吸收系数约为标准值的9％，相对误差约为91％。通过用蒙特卡洛模拟校正PVE，可以抑制脑外层的影响，整个数据集的平均相对误差仅提高到大约6％。因此，