基于近红外光谱技术的脑功能活动信号提取方法研究
|
摘要
近红外光谱技术能够提供基于血红蛋白浓度变化的血液动力学信息,从而反映大脑皮质的血氧代谢状况,可用于脑功能活动的检测,被称为功能近红外光谱技术。与其它脑功能检测技术,如脑电图、脑磁图、正电子发射层析成像、以及功能磁共振成像等相比,近红外光谱技术具有使用方便、易实施、时间分辨率高、安全、便宜等优点,有非常广阔的应用前景。
近红外脑功能检测会受到心动周期、呼吸、自发性低频振荡以及超低频振荡等人体生理活动的干扰,这种生理干扰不仅出现在头皮、颅骨和脑脊液等外层脑组织中,也出现在脑灰质和脑白质等深层脑组织中,严重影响脑功能信号的准确提取。因此,本文针对利用近红外光谱技术进行脑功能信号提取中存在的生理干扰问题,研究相应的解决方法,从而提高脑功能信号的检测精度,促进功能近红外光谱技术的开发与应用。本文的主要研究内容体现在如下几个方面:
(1)在进行脑功能检测时,近红外光谱技术无法获得真实的脑功能信号,并且部分容积效应会造成测量信号远低于真实信号,从而难以定量分析脑功能信号提取方法的有效性。因此,本文基于五层脑部模型对脑功能活动进行模拟,利用MonteCarlo方法仿真近红外光在模型中的传输过程,通过补偿部分容积效应,开发了可用于对脑功能信号提取方法进行定量分析的仿真软件包。
(2)单距测量方法具有探头结构简单和易于实现的优点,常用于血液动力学变化量的检测及基于阵列式光源检测器布局的脑功能成像研究。基于该方法的脑功能信号提取通常利用低通滤波技术抑制心动周期引起的生理干扰,但由于呼吸信号在频率上和脑功能信号有一定的重叠且是非平稳信号,使得呼吸干扰难以抑制。因此,本文提出基于经验模态分解的脑功能信号提取方法,该方法具有自适应时频特性,能够剔除心动周期和呼吸引起的生理干扰,从而提高脑功能信号的检测精度。利用自行开发的软件包,通过仿真验证了该方法在脑功能信号提取方面的有效性。
(3)相比于心动周期和呼吸,低频振荡和超低频振荡等干扰信号与脑功能信号的频带严重重叠,采用常规的滤波方法不能消除这样的生理干扰。脑功能活动发生在深层大脑皮质,而生理干扰来自各种不同的脑组织。考虑探测深度与探测距离相关,提出基于多距测量方法的递归最小二乘脑功能信号提取方法。在该方法中,近端检测器用于获取参考信号,远端检测器用于获取期望信号,利用递推最小二乘自适应滤波技术进行处理从而完成脑功能信号的提取,并通过对比研究了递推最小二乘算法和最小均方算法的检测精度和收敛速率。此外,考虑基于多距测量方法的自适应滤波对近红外脑功能信号的提取精度与探头布局密切相关,进一步分析了多距测量方法中探头布局对测量结果的影响及探头布局对外层脑组织厚度的敏感性。仿真结果表明基于多距测量方法的递推最小二乘自适应滤波能够有效抑制生理干扰,对脑功能信号的检测精度和收敛速率均明显优于最小均方自适应滤波,并从统计意义上给出了不同探头布局和不同外层脑组织厚度时脑功能信号提取结果的均方误差。
(4)近红外脑功能检测的生理干扰来源于人体不同的生理活动,当脑组织的非均匀性严重时,不同的生理活动在空间不同位置对生理干扰的影响也不尽相同。针对该问题,利用基于多距测量方法的自适应滤波能够实现对生理干扰的抑制,但并不能获得很好的检测精度。因此,针对多距测量方法,提出基于经验模态分解优化算法的脑功能信号提取方法。该方法首先对近端检测器测得的血液动力学变化进行经验模态分解,将分解的固有模态函数赋予不同的权系数以估计期望信号中的生理干扰,并通过递推最小二乘算法优化权系数。仿真结果表明优化算法能够依据生理干扰与固有模态函数的相关度自适应地调节权系数,在脑组织非均匀性严重时其脑功能信号检测精度优于最小二乘自适应滤波。
(5)为了验证近红外光谱技术在脑功能信号检测中的有效性,本文基于连续波光谱技术和多距测量方法设计了近红外组织氧检测系统。通过离体模型实验和在体前臂阻断实验对近红外组织氧检测系统的工作性能进行了分析。针对音乐刺激诱发的颞叶区血液动力学变化,进行了基于听觉组块设计的脑功能实验研究。利用组织氧检测系统对实验过程中漫反射光强进行实时监测,并采用递推最小二乘自适应滤波对颞叶区的血液动力学变化进行了深入分析。听觉刺激的实验结果表明,颞叶区对音乐刺激敏感,经过对比原始的与提取的血液动力学变化,证明了基于多距测量方法的递推最小二乘自适应滤波在脑功能检测中可行性和有效性。
Near infrared spectroscopy (NIRS) allows the non-invasive measurement of haemody-namic variables and is particularly suited to the detection of changes in concentrationsof oxy- and deoxy-haemoglobin in the brain, thereby providing insights into metabol-ic events in the cerebral cortex. Consequently, NIRS has been developed to measurebrain activity, leading to what has become the well-recognised method of functional near-infrared spectroscopy (fNIRS). fNIRS may be compared with other techniques, such aselectroencephalography (EEG), magnetoencephalography (MEG), positron emission to-mography (PET), and functional magnetic resonance imaging (fMRI). It does appear tohave several advantages over these other methods, such as portability, fewer physicalrestrictions and greater practicality, good temporal resolution, safety, and inexpensiveinstrumentation, and thus has a very broad application prospect. However, there are prob-lems in using fNIRS due to the presence of physiological interference.
The physiological interference when using fNIRS arises mainly from perturbationscaused by cardiac events, breathing, low frequency oscillations (LFOs), and very lowfrequency oscillations (VLFOs). All of these interference sources are located both in thevasculature of the superficial layer of the brain and deeper inside the brain. This has meantthat without appropriate interference reduction the full potential of fNIRS has not yet beenrealised. Therefore, this thesis studies several practical methodologies to overcome thephysiological interference problem in fNIRS, aiming to improve the detection accuracyof brain activity measurement and promote the further development and utilization of themethod.
The main contents of this dissertation are as follows:
(1) A justification for the use of Monte Carlo simulation is given. A truly rigorousevaluation of fNIRS in vivo requires an uncontaminated evoked brain activity responsesignal as a standard, which, unfortunately, is unavailable. In addition, the partial volumeeffect (PVE) cannot be precisely compensated for in vivo and the quantitative comparisonof the recovery response and the true response of brain activity is therefore difficult. Thus,the Monte Carlo method, based on a five-layered adult head model, was implemented tosimulate the brain activity process for optical measurement. By compensating for the PVE, a simulation software package was developed to serve as the evaluation tool fordifferent methods for the extraction of brain activity measurements.
(2) The use of Empirical Mode Decomposition (EMD) for brain signal extraction isdescribed. The single-distance NIRS probe configuration is often used to measure thehaemodynamic changes, both for monitoring and for imaging based on grids of sourcesand detectors, because it has the advantages of the simplicity of the optical probe andgreater practicality. Low pass filtering techniques have been used in attempts to suppressphysiological interference and these have been moderately successful for removal of theinterference caused by cardiac oscillations. However, low pass filtering may not be appro-priate for other specific physiological noise, such as that produced by breathing since suchnoise is difficult to be distinguished from the genuine haemodynamic response to brainactivity by frequency characteristics alone and thus it is not possible to design the low passfilter with a fixed cut-off frequency. Therefore, a methodology based on EMD is proposedto extract the signal of brain activity for single-distance measurement. The accuracy ofthe brain activity measurement is improved by utilizing EMD because it can be used toremove interference arising from the cardiac events and breathing. The effectiveness ofthis methodology has been proved by means of the software package.
(3) The application of Recursive Least Squares (RLS) adaptive filtering is described.Compared with cardiac and respiratory interference, the suppression of LFOs and VL-FOs is relatively difficult with ordinary filtering techniques because these frequencies andthose of the functional activity may severely overlay each other. In fNIRS measurementvery useful information may be in the deep tissue (gray matter) and light inevitably inter-acts with blood in layers other than gray matter. Considering that the penetration depth ofNIRS is related to the source-detector separation, a methodology of combining a multi-distance probe and recursive least square (RLS) adaptive filtering is proposed. We madethe measurement acquired from NIRS short source-detector separation as the referencesignal and the measurement acquired from NIRS long source-detector separation as thedesired signal. The least mean square (LMS) and RLS algorithms are implemented tocompare the accuracy and the convergence rate. We derived measurements by adoptingdifferent interoptode distances, which is relevant to the process of optimizing the NIRSprobe configuration. The in?uence of superficial layer thickness on the performance of theRLS algorithm was also investigated. The simulation results demonstrated that the RLS algorithm has a faster convergence and smaller mean squared error (MSE) than the LMSalgorithm. The MSE for different probe configuration and superficial layer thickness arealso calculated based on statistical methods.
(4) The combination of EMD and RLS was explored. Physiological interference can beinduced by different physiological phenomena and thus it contains multiple components.When the brain exhibits some haemodynamic heterogeneity, the different interferencecomponents may produce dissimilarities between the superficial layers and the cortex, orin different locations. In our study presented here we adopt the multidistance measure-ment method and a theoretical analysis of global interference reduction based on EMDand the least squares criterion. The short-distance fNIRS measurement is treated as com-prising of superficial haemodynamic changes induced by physiological ?uctuations andthe long-distance fNIRS measurement is the functional haemodynamic response contam-inated by global interference. By decomposing superficial haemodynamic ?uctuationswith the EMD algorithm, we separated the interference into different intrinsic mode func-tions (IMFs) possessing distinct frequency characteristics. The recursive least squaresmethod was then used to adjust the corresponding weighting coefficients to estimate glob-al interference with the obtained IMFs. The experimental results demonstrate that optimalalgorithms have higher precision that RLS adaptive filter when the brain tissue presentssome degree of heterogeneity.
(5) In vivo measurements with multi-distance NIRS were investigated. To further studythe brain activity with fNIRS and evaluate the effectiveness of the proposed method, aNIRS system was developed based on a multi-distance measurement configuration andcontinuous wave spectroscopy. The performance of the system was verified by the in vit-ro model experiment and in vivo forearm occlusion experiments. Subsequently a block-design experiment was conducted on auditory stimuli and the evoked response of thecortex in the temporal region was continuously monitored and further analyzed by theRLS algorithm. The experimental results show that the temporal area is sensitive to mu-sic stimuli. The comparison of the original results and the RLS results demonstrate thefeasibility and effectiveness of RLS adaptive filtering for fNIRS.
近红外脑功能检测会受到心动周期、呼吸、自发性低频振荡以及超低频振荡等人体生理活动的干扰,这种生理干扰不仅出现在头皮、颅骨和脑脊液等外层脑组织中,也出现在脑灰质和脑白质等深层脑组织中,严重影响脑功能信号的准确提取。因此,本文针对利用近红外光谱技术进行脑功能信号提取中存在的生理干扰问题,研究相应的解决方法,从而提高脑功能信号的检测精度,促进功能近红外光谱技术的开发与应用。本文的主要研究内容体现在如下几个方面:
(1)在进行脑功能检测时,近红外光谱技术无法获得真实的脑功能信号,并且部分容积效应会造成测量信号远低于真实信号,从而难以定量分析脑功能信号提取方法的有效性。因此,本文基于五层脑部模型对脑功能活动进行模拟,利用MonteCarlo方法仿真近红外光在模型中的传输过程,通过补偿部分容积效应,开发了可用于对脑功能信号提取方法进行定量分析的仿真软件包。
(2)单距测量方法具有探头结构简单和易于实现的优点,常用于血液动力学变化量的检测及基于阵列式光源检测器布局的脑功能成像研究。基于该方法的脑功能信号提取通常利用低通滤波技术抑制心动周期引起的生理干扰,但由于呼吸信号在频率上和脑功能信号有一定的重叠且是非平稳信号,使得呼吸干扰难以抑制。因此,本文提出基于经验模态分解的脑功能信号提取方法,该方法具有自适应时频特性,能够剔除心动周期和呼吸引起的生理干扰,从而提高脑功能信号的检测精度。利用自行开发的软件包,通过仿真验证了该方法在脑功能信号提取方面的有效性。
(3)相比于心动周期和呼吸,低频振荡和超低频振荡等干扰信号与脑功能信号的频带严重重叠,采用常规的滤波方法不能消除这样的生理干扰。脑功能活动发生在深层大脑皮质,而生理干扰来自各种不同的脑组织。考虑探测深度与探测距离相关,提出基于多距测量方法的递归最小二乘脑功能信号提取方法。在该方法中,近端检测器用于获取参考信号,远端检测器用于获取期望信号,利用递推最小二乘自适应滤波技术进行处理从而完成脑功能信号的提取,并通过对比研究了递推最小二乘算法和最小均方算法的检测精度和收敛速率。此外,考虑基于多距测量方法的自适应滤波对近红外脑功能信号的提取精度与探头布局密切相关,进一步分析了多距测量方法中探头布局对测量结果的影响及探头布局对外层脑组织厚度的敏感性。仿真结果表明基于多距测量方法的递推最小二乘自适应滤波能够有效抑制生理干扰,对脑功能信号的检测精度和收敛速率均明显优于最小均方自适应滤波,并从统计意义上给出了不同探头布局和不同外层脑组织厚度时脑功能信号提取结果的均方误差。
(4)近红外脑功能检测的生理干扰来源于人体不同的生理活动,当脑组织的非均匀性严重时,不同的生理活动在空间不同位置对生理干扰的影响也不尽相同。针对该问题,利用基于多距测量方法的自适应滤波能够实现对生理干扰的抑制,但并不能获得很好的检测精度。因此,针对多距测量方法,提出基于经验模态分解优化算法的脑功能信号提取方法。该方法首先对近端检测器测得的血液动力学变化进行经验模态分解,将分解的固有模态函数赋予不同的权系数以估计期望信号中的生理干扰,并通过递推最小二乘算法优化权系数。仿真结果表明优化算法能够依据生理干扰与固有模态函数的相关度自适应地调节权系数,在脑组织非均匀性严重时其脑功能信号检测精度优于最小二乘自适应滤波。
(5)为了验证近红外光谱技术在脑功能信号检测中的有效性,本文基于连续波光谱技术和多距测量方法设计了近红外组织氧检测系统。通过离体模型实验和在体前臂阻断实验对近红外组织氧检测系统的工作性能进行了分析。针对音乐刺激诱发的颞叶区血液动力学变化,进行了基于听觉组块设计的脑功能实验研究。利用组织氧检测系统对实验过程中漫反射光强进行实时监测,并采用递推最小二乘自适应滤波对颞叶区的血液动力学变化进行了深入分析。听觉刺激的实验结果表明,颞叶区对音乐刺激敏感,经过对比原始的与提取的血液动力学变化,证明了基于多距测量方法的递推最小二乘自适应滤波在脑功能检测中可行性和有效性。
Near infrared spectroscopy (NIRS) allows the non-invasive measurement of haemody-namic variables and is particularly suited to the detection of changes in concentrationsof oxy- and deoxy-haemoglobin in the brain, thereby providing insights into metabol-ic events in the cerebral cortex. Consequently, NIRS has been developed to measurebrain activity, leading to what has become the well-recognised method of functional near-infrared spectroscopy (fNIRS). fNIRS may be compared with other techniques, such aselectroencephalography (EEG), magnetoencephalography (MEG), positron emission to-mography (PET), and functional magnetic resonance imaging (fMRI). It does appear tohave several advantages over these other methods, such as portability, fewer physicalrestrictions and greater practicality, good temporal resolution, safety, and inexpensiveinstrumentation, and thus has a very broad application prospect. However, there are prob-lems in using fNIRS due to the presence of physiological interference.
The physiological interference when using fNIRS arises mainly from perturbationscaused by cardiac events, breathing, low frequency oscillations (LFOs), and very lowfrequency oscillations (VLFOs). All of these interference sources are located both in thevasculature of the superficial layer of the brain and deeper inside the brain. This has meantthat without appropriate interference reduction the full potential of fNIRS has not yet beenrealised. Therefore, this thesis studies several practical methodologies to overcome thephysiological interference problem in fNIRS, aiming to improve the detection accuracyof brain activity measurement and promote the further development and utilization of themethod.
The main contents of this dissertation are as follows:
(1) A justification for the use of Monte Carlo simulation is given. A truly rigorousevaluation of fNIRS in vivo requires an uncontaminated evoked brain activity responsesignal as a standard, which, unfortunately, is unavailable. In addition, the partial volumeeffect (PVE) cannot be precisely compensated for in vivo and the quantitative comparisonof the recovery response and the true response of brain activity is therefore difficult. Thus,the Monte Carlo method, based on a five-layered adult head model, was implemented tosimulate the brain activity process for optical measurement. By compensating for the PVE, a simulation software package was developed to serve as the evaluation tool fordifferent methods for the extraction of brain activity measurements.
(2) The use of Empirical Mode Decomposition (EMD) for brain signal extraction isdescribed. The single-distance NIRS probe configuration is often used to measure thehaemodynamic changes, both for monitoring and for imaging based on grids of sourcesand detectors, because it has the advantages of the simplicity of the optical probe andgreater practicality. Low pass filtering techniques have been used in attempts to suppressphysiological interference and these have been moderately successful for removal of theinterference caused by cardiac oscillations. However, low pass filtering may not be appro-priate for other specific physiological noise, such as that produced by breathing since suchnoise is difficult to be distinguished from the genuine haemodynamic response to brainactivity by frequency characteristics alone and thus it is not possible to design the low passfilter with a fixed cut-off frequency. Therefore, a methodology based on EMD is proposedto extract the signal of brain activity for single-distance measurement. The accuracy ofthe brain activity measurement is improved by utilizing EMD because it can be used toremove interference arising from the cardiac events and breathing. The effectiveness ofthis methodology has been proved by means of the software package.
(3) The application of Recursive Least Squares (RLS) adaptive filtering is described.Compared with cardiac and respiratory interference, the suppression of LFOs and VL-FOs is relatively difficult with ordinary filtering techniques because these frequencies andthose of the functional activity may severely overlay each other. In fNIRS measurementvery useful information may be in the deep tissue (gray matter) and light inevitably inter-acts with blood in layers other than gray matter. Considering that the penetration depth ofNIRS is related to the source-detector separation, a methodology of combining a multi-distance probe and recursive least square (RLS) adaptive filtering is proposed. We madethe measurement acquired from NIRS short source-detector separation as the referencesignal and the measurement acquired from NIRS long source-detector separation as thedesired signal. The least mean square (LMS) and RLS algorithms are implemented tocompare the accuracy and the convergence rate. We derived measurements by adoptingdifferent interoptode distances, which is relevant to the process of optimizing the NIRSprobe configuration. The in?uence of superficial layer thickness on the performance of theRLS algorithm was also investigated. The simulation results demonstrated that the RLS algorithm has a faster convergence and smaller mean squared error (MSE) than the LMSalgorithm. The MSE for different probe configuration and superficial layer thickness arealso calculated based on statistical methods.
(4) The combination of EMD and RLS was explored. Physiological interference can beinduced by different physiological phenomena and thus it contains multiple components.When the brain exhibits some haemodynamic heterogeneity, the different interferencecomponents may produce dissimilarities between the superficial layers and the cortex, orin different locations. In our study presented here we adopt the multidistance measure-ment method and a theoretical analysis of global interference reduction based on EMDand the least squares criterion. The short-distance fNIRS measurement is treated as com-prising of superficial haemodynamic changes induced by physiological ?uctuations andthe long-distance fNIRS measurement is the functional haemodynamic response contam-inated by global interference. By decomposing superficial haemodynamic ?uctuationswith the EMD algorithm, we separated the interference into different intrinsic mode func-tions (IMFs) possessing distinct frequency characteristics. The recursive least squaresmethod was then used to adjust the corresponding weighting coefficients to estimate glob-al interference with the obtained IMFs. The experimental results demonstrate that optimalalgorithms have higher precision that RLS adaptive filter when the brain tissue presentssome degree of heterogeneity.
(5) In vivo measurements with multi-distance NIRS were investigated. To further studythe brain activity with fNIRS and evaluate the effectiveness of the proposed method, aNIRS system was developed based on a multi-distance measurement configuration andcontinuous wave spectroscopy. The performance of the system was verified by the in vit-ro model experiment and in vivo forearm occlusion experiments. Subsequently a block-design experiment was conducted on auditory stimuli and the evoked response of thecortex in the temporal region was continuously monitored and further analyzed by theRLS algorithm. The experimental results show that the temporal area is sensitive to mu-sic stimuli. The comparison of the original results and the RLS results demonstrate thefeasibility and effectiveness of RLS adaptive filtering for fNIRS.
引文
1徐可欣,高峰,赵慧娟.生物医学光子学[M].北京:科学出版社, 2007:1–185.
2戴丽娟.脑组织参数近红外实时在位微创测量技术及其应用研究[D].南京:南京航空航天大学精密仪器与机械学科博士学位论文,2008:1–25.
3邓玉林,李勤.生物医学工程学[M].北京:科学出版社, 2007:80–104.
4张连顺.光与生物组织的相互作用及生物组织光学特性参数测量[D].天津:南开大学凝聚态物理学科博士学位论文,2003:1–9.
5 Wang L H. Monte Carlo Modeling of Light Transport in Multi-layered Tissues inStandard C[D]. Texas:Ph.D Dissertion of Texas A&M University, 1998:1–168.
6 Cerussi A E. Quantitative Frequency-domain Fluorescence Spectroscopy in Tissuesand Tissue-like Media[D]. Urbana-Champaign:Ph.D Dissertion of University ofIllinois at Urbana-Champaign, 1999:1–233.
7 Matcher S J, Cope M, Delpy D T. In Vivo Measurements of the Wavelength Depen-dence of Tissue-scattering Coefficients between 760 and 900 Nm Measured withTime-resolved Spectroscopy[J]. Applied Optics, 1997, 36(1):386–396.
8 Pe′ry E, Blondel W C P M, Thomas C. Monte Carlo Modeling of Multilayer Phan-toms with Multiple Fluorophores: Simulation Algorithm and Experimental Valida-tion[J]. Journal of Biomedical Optics, 2009, 14(2):024048.
9 Umeyama S, Yamada T. Monte Carlo Study of Global Interference Cancellation byMultidistance Measurement of Near-infrared Spectroscopy[J]. Journal of Biomed-ical Optics, 2009, 14(6):064025.
10腾轶超,叶大田,李岳,等.无损检测组织氧饱和度的近红外光学传感器的优化设计研究[J].光谱学与光谱分析,2008,28(4):953–957.
11刘蓉,谷筱玉,徐可欣.近红外光谱无创血糖测量中背景扣除方法的研究[J].光谱学与光谱分析,2008,28(8):1772–1775.
12李刚,刘玉良,林凌,等.利用统计处理方法提高动态光谱的检测精度[J].光谱学与光谱分析,2007,27(9):1669–1672.
13郑毅,骆清铭,刘谦,等.适于脑功能活动检测的便携式近红外光谱仪的研制[J].中国生物医学工程学报,2007,26(6):898–902.
14张连顺,张春平,王新宇,等.两层生物组织光学特性参数无损测量的模拟研究[J].发光学报,2003,24(1):56–59.
15戴丽娟,王慧南,钱志余,等.血液可见吸收光谱与血氧参数神经网络估算法[J].光谱学与光谱分析,2008,28(7):1468–1472.
16王晶,张镇西,徐正红,等.基于anep染料荧光光谱迁移的单波长心脏光学标测系统[J].光谱学与光谱分析,2008,28(3):617–620.
17张镇西.“生物医学光子学[J].
18姚翠萍,张镇西.激光与组织的相互作用[J].激光生物学报, 1999, 23(6):344–349.
19 Xu L Q, Li H, Xie S S. Theoretical Analysis of Backscattered Polarization Patternsof Turbid Media Containing Glucose[J]. Chinese Optics Letters, 2007, 5(2):102–104.
20 Jo¨bsis F F. Noninvasive Infrared Monitoring of Cerebral and Myocardial Oxy-gen Sufficiency and Circulatory Parameters[J]. Chinese Optics Letters, 1977,198(4323):1264–1267.
21曹传花.便携式近红外脑功能成像系统的性能测试[D].武汉:华中科技大学生物医学工程学科硕士学位论文,2006:1–10.
22黄岚.近红外组织氧绝对量检测的研究及应用[D].北京:清华大学生物医学工程学科博士学位论文,2004:1–89.
23 Tseng S, Grant A, Durkin A J. In Vivo Determination of Skin Near-infrared OpticalProperties Using Diffuse Optical Spectroscopy[J]. Journal of Biomedical Optics,2008, 13(1):014016.
24 Laurent C, Jbnsson B, Vegfors M, et al. Non-invasive Measurement of SystolicBlood Pressure on the Arm Utilising Photoplethysmography: Development of theMethodology[J]. Medical & Biological Engineering & Computing, 2005, 43:131–135.
25 Yang Y, Landry M R, Soyemi O O, et al. Simultaneous Correction of the In?uenceof Skin Color and Fat on Tissue Spectroscopy by Use of a Two-distance Fiber-opticProbe and Orthogonalization Technique[J]. Optics Letters, 2005, 30(17):2269–2271.
26 El-Desoky A E, Seifalian A, Cope M, et al. Changes in Tissue Oxygenation ofthe Porcine Liver Measured by Near-infrared Spectroscopy[J]. Liver TransplantSurgery, 1999, 5:219–226.
27 Ronald X X, Bo Q, Jimmy J M, et al. Development of a Handheld Near-infraredImager for Dynamic Characterization of in Vivo Biological Tissue Systems[J]. Ap-plied Optics, 2007, 46(30):7442–7451.
28 Cheng X F, Mao J, Bush R, et al. Breast Cancer Detection by Mapping HemoglobinConcentration and Oxygen Saturation[J]. Applied Optics, 2003, 42(31):6412–6421.
29 Rea P A, Crowe J, Wickramasinghe Y, et al. Non-invasive Optical Methods forthe Study of Cerebral Metabolism in the Human Newborn: A Technique for theFuture?[J]. Journal of Medical Engineering Technology, 1985, 9(4):160–166.
30段相林,郭炳冉,辜清.人体组织学与解剖学[M].北京:高等教育出版社,2006:262–297.
31赵军.新生儿大脑组织光学参数的无损检测[D].北京:清华大学生物医学工程学科博士学位论文,2004:1–13.
32邱一华,彭聿平.生理学[M].北京:科学出版社, 2004:1–174.
33 Kiening K L, Hartl R, Unterberg A W, et al. Brain Tissue Po2-monitoring inComatose Patients: Implications for Therapy[J]. Neurological Research, 1997,19(3):233–240.
34陈婷方.脑红蛋白神经保护功能及其机制的初步研究[D].北京:中国人民解放军军事医学科学院病理学与病理生理学学科博士学位论文,2008:1–10.
35 Hintz S R. Near-infrared Spectroscopy: Neonatal and Perinatal Applications[J].NeoReviews, 2001, 2:22–28.
36 Gopinath S P, Robertson C S, Grossman R G, et al. Near-infrared SpectroscopicLocalization of Intracranial Hematomas[J]. Journal of Neurosurgery, 1993, 79:43–47.
37 Steen R G, Kitagishi K, Morgan K. In Vivo Measurement of Tumor Blood Oxy-genation by Near-infrared Spectroscopy: Immediate Effects of Pentobarbital Over-dose of Carmustine Treatment[J]. Journal of Neuro-Oncology, 1994, 22:209–220.
38 Hull E L, Conover D L, Foster T H. Carbogeninduced Changes in Rat MammaryTumor Oxygenation Reported by Near-infrared Spectroscopy[J]. British Journal ofCancer, 1999, 79:1709–1716.
39 Gratton E, Toronov V, Wolf U, et al. Measurement of Brain Activity by Near-infrared Light[J]. Journal of Biomedical Optics, 2005, 10(1):011008.
40 Sato H, Kiguchi M, Kawaguchi F, et al. Practicality of Wavelength Selection to Im-prove Signal-to-noise Ratio in Near-infrared Spectroscopy[J]. NeuroImage, 2003,21:1554–1562.
41 Isobe K, Kusaka T, Nagano K, et al. Functional Imaging of the Brain in SedatedNewborn Infants Using Near Infrared Topography During Passive Knee Movemen-t[J]. Neuroscience Letters, 2001, 299:221–224.
42李鹏程,曾绍群,骆清铭.脑皮层功能活动与病理状态光学检测方法研究进展[J].中国生物医学工程学报,2008,27(2):164–168.
43 Yang Y, Soyemi O O, Scott P J, et al. Quantitative Measurement of Muscle Oxy-gen Saturation without In?uence from Skin and Fat Using Continuous-wave NearInfrared Spectroscopy[J]. Optics Express, 2007, 15(21):13715–13730.
44 Sevick E M, Chance B, Leigh J, et al. Quantitation of Time and Frequency Re-solved Optical Spectra for the Determination of Tissue Oxygenation[J]. AnalyticalBiochemistry, 1991, 195(2):330–351.
45赵军,丁海曙,阮曼奇,等.利用频域近红外光谱仪和磁共振谱仪测量骨骼肌能量代谢[J].光谱学与光谱分析,2005,25(6):861–865.
46龚辉,李成军,李婷,等.前额叶皮层工作记忆作用的近红外光学成像[J].中国科学,2007,37(增):110–117.
47 De Blasi R A, Ferrari M, Natali A, et al. Noninvasive Measurement of ForearmBlood Flow and Oxygen Consumption by Nearinfrared Spectroscopy[J]. Journalof Applied Physiology, 1994, 76(3):1388–1393.
48 Shiga T, Tanabe K, Nakase Y, et al. Development of a Portable Tissue OximeterUsing Near Infrared Spectroscopy[J]. Medical & Biological Engineering & Com-puting, 1995, 33(4):622–626.
49 Chance B, Dait M T, Zhang C, et al. Recovery from Exercise-induced Desaturationin the Quadriceps Muscles of Elite Competitive Rowers[J]. American Journal ofPhysiology, 1992, 262(3):C766–C775.
50 Colier W N, Meeuwsen I B, Degens H, et al. Determination of Oxygen Consump-tion in Muscle During Exercise Using Near Infrared Spectroscopy[J]. Acta Anaes-thesiologica Scandinavica, 1995, 39(S107):151–155.
51 Thavasothy M, Broadhead M, Elwell C, et al. A Comparison of Cerebral Oxygena-tion as Measured by the Niro 300 and the Invos 5100 Near-infrared Spectropho-tometers[J]. Anaesthesia, 2002, 57(10):999–1006.
52 Quaresima V, Homma S, Azuma K, et al. Calf and Shin Muscle OxygenationPatterns and Femoral Artery Blood Flow During Dynamic Plantar Flexion Exercisein Humans[J]. European Journal of Applied Physiology, 2001, 84(5):387–394.
53 Franceschini M A, Boas D A, Zourabian A, et al. Near-infrared Spiroximetry: Non-invasive Measurements of Venous Saturation in Piglets and Human Subjects[J].Journal of Applied Physiology, 2002, 92(1):372–384.
54 Hoshi Y. Functional Near-infrared Optical Imaging: Utility and Limitations inHuman Brain Mapping[J]. Psychophysiology, 2003, 40(4):511–520.
55 Kennan R P, Horovitz S G, Maki A, et al. Simultaneous Recording of Event-relatedAuditory Oddball Response Using Transcranial Near Infrared Optical Topographyand Surface Eeg[J]. Neuroimage, 2002, 16(3):587–592.
56 Wolf M, Wolf U, Choi J H, et al. Functional Frequency-domain Near-infraredSpectroscopy Detects Fast Neuronal Signal in the Motor Cortex[J]. Neuroimage,2002, 17(4):1868–1875.
57 Hebden J C, Gibson A, Yusof R M, et al. Three-dimensional Optical Tomogra-phy of the Premature Infant Brain[J]. Physics in Medicine and Biology, 2002,47(32):4155–4166.
58 Cubeddu R, Biscotti G, Pifferi A, et al. Dual-wavelength, 8-channel Time-resolvedOximetry for Functional Muscle Studies[C]//Proceedings of Asian Symposium onBiomedical Optics and Photomedicine. 2002:198–199.
59李岳,丁海曙,黄岚,等.近红外光谱方法在颌面外科皮瓣移植术后监测中的应用[J].光谱学与光谱分析,2005,25(3):377–380.
60王峰, 李炜,林方,等.用近红外光谱技术实现生物组织含氧量的无损检测[J].清华大学学报(自然科学版),1999,39(7):16–19.
61黄岚,田丰华,丁海曙,等.用近红外光谱对组织氧测量方法的研究[J].红外与毫米波学报,2003,22(5):379–383.
62腾轶超,丁海曙,黄岚,等.外层覆盖下组织氧饱和度的无损检测及可信度评定[J].自然科学进展,2007,17(8):1138–1143.
63腾轶超,丁海曙,龚庆成,等.红外光谱监测体外循环手术中脑组织氧合状况的研究[J].光谱学与光谱分析,2006,26(5):828–832.
64丁海曙,腾轶超.组织血氧参数近红外无损检测技术及自主创新之路[J].激光与光电子学进展,2007,44(9):14–31.
65腾轶超.近红外空间分辨光谱技术及其在脑氧无损检测中的应用[D].北京:清华大学生物医学工程博士学位论文,2006:1–100.
66李婷,李黎,杜鹏,等.视觉非随意注意的近红外脑功能成像技术与事件相关脑电位检测技术研究[J].光学学报,2007,27(3):531–535.
67杨宏宇,周振宇,刘云,等.近红外光学成像技术检测视觉搜索任务期间大脑前额叶氧合血红蛋白水平变化[J].航天医学与医学工程,2007,20(3):209–212.
68吴太虎,徐可欣,刘庆珍,等.近红外光谱法无创测量人体血红蛋白浓度[J].激光生物学报,2006,15(2):204–208.
69相韶霞,林凌,王艳秋,等.近红外光谱组织血氧检测结果的定量化方法[J].光学技术,2001,27(5):451–455.
70赵会娟,阎长斐,张顺起,等.基于微扰蒙特卡罗的薄层状组织光学参数重构技术[J].纳米技术与精密工程,2009,7(3):254–258.
71王利军,刘迎,田会娟,等.利用微区空间分辨漫反射测量组织光学特性[J].光电子·激光,2009,20(1):122–125.
72林麟,林凌,李刚.一种新型算法在组织光学参数测量系统中的应用[J].北京生物医学工程,2007,26(1):84–86.
73万柏坤,刘延刚,明东,等.基于脑电特征的多模式想象动作识别[J].天津大学学报,2010,43(10):895–900.
74许棠.生物组织中的光传输及生物组织光学特性参数测量的研究[D].天津:南开大学凝聚态物理学科博士学位论文,2004:1–141.
75来建成.生物组织的光学描述与光传输规律研究[D].南京:南京理工大学光学工程学科博士学位论文,2005:1–103.
76陈卫国,李鹏程,骆清铭,等.用近红外光拓扑图技术短期预测脑梗塞[J].光子学报,2000,29(8):673–677.
77罗斌.漫射光成像理论模型及算法的性能研究[D].杭州:浙江大学光学工程学科博士学位论文,2007:1–112.
78李婷.光在三维结构组织中传输的monte Carlo模拟及脑功能成像研究[D].武汉:华中科技大学生物医学工程学科博士学位论文,2010:1–131.
79徐国栋,陈刚,周超彦,等.血氧含量的近红外测定及其在运动实践中的应用[J].武汉体育学院学报,2004,38(1):34–38.
80 Saager R B, Berger A J. Direct Characterization and Removal of Interfering Ab-sorption Trends in Two-layer Turbid Media[J]. Journal of the Optical Society ofAmerica A: Optics, 2005, 22(9):1874–1882.
81 Strangman G, Boas D A, Sutton J P. Non-invasive Neuroimaging Using Near-infrared Light[J]. Biological Psychiatry, 2002, 52:679–693.
82刘海龙.生物医学信号处理[M].北京:化学工业出版社, 2006:1–322.
83 Reite M, Zimmerman J E, Edrich J, et al. The Human Magnetoece- Phalogram:Some Eeg and Related Correlations[J]. Electroencephalography and Clinical Neu-rophysiology, 1976, 40(1):59–66.
84刘晓丹,黄力.脑磁图、磁源性成像在脑肿瘤中的研究进展[J].国际医学放射学杂志,2010,33(3):205–208.
85伍国锋,张文渊.脑电波产生的神经生理机制[J].临床脑电学杂志, 2000,9(3):188–190.
86胡洁,胡净,黄定君.脑磁图研究进展[J].生物医学工程与临床, 2003,7(3):181–184.
87 Robert L S. History and Future Directions of Human Brain Mapping and FunctionalNeuroimaging[J]. Acta Psychologica, 2001, 107(1-3):9–42.
88颜建华.正电子发射层析图像重建算法研究[D].武汉:华中科技大学微电子学与固体电子学学科博士学位论文,2007:1–12.
89庄华梅,何德.核磁共振技术及其在生命科学中的应用[J].生物磁学, 2005,5(4):59–61.
90 Matcher S J, Elwell C E, Cooper C E, et al. Performance Comparison of SeveralPublished Tissue Near-infrared Spectroscopy Algorithms[J]. Analytical Biochem-istry, 1995, 227:54–68.
91 Rolfe P. In Vivio Near-infrared Spectroscopy[J]. Annual Review of BiomedicalEngineering, 2000, 2:715–754.
92 Panerai R B. Assessment of Cerebral Pressure Autoregulation in Humans - a Re-view of Measurement Methods[J]. Physiological Measurement, 1998, 19(3):305–338.
93骆清铭.光电技术在生物医学中的应用–现状与发展[J].光学与光电技术,2003, 1(1):7–14.
94 Benaron D A, Hintz S R, Villringer A, et al. Noninvasive Functional Imaging ofHuman Brain Using Light[J]. Journal of Cerebral Blood Flow and Metabolism,2000, 20(3):469–477.
95 Toronov V, Webb A, Choi J H, et al. Investigation of Human Brain Hemodynamicsby Simultaneous Near-infrared Spectroscopy and Functional Magnetic ResonanceImaging[J]. Medical Physics, 2001, 28(4):521–527.
96李成军.近红外光谱技术用于前额叶皮层工作记忆作用的研究[D].武汉:华中科技大学生物医学工程学科博士学位论文,2005:1–83.
97 Kohno S, Miyai I, Seiyama A, et al. Removal of the Skin Blood Flow Artifact inFunctional Near-infrared Spectroscopic Imaging Data Through Independent Com-ponent Analysis[J]. Journal of Biomedical Optics, 2007, 12(6):062111.
98 Churchland P S, Sejnowski T J. Perspectives on Cognitive Neuroscience[J]. Sci-ence, 1988, 242:741–745.
99 Hoshi Y, Tamura M. Detection of Dynamic Changes in Cerebral Oxygenation Cou-pled to Neuronal Function During Mental Work in Man[J]. Neuroscience Letters,1993, 150(1):5–8.
100 Kato T, Kamei A, Takahashi S, et al. Human Visual Cortical Function During Pho-tonic Stimulation Monitoring by Means of Near-infrared Spectroscopy[J]. Journalof Cerebral Blood Flow and Metabolism, 1993, 13:516–520.
101 Okada F, Tokumitsu Y, Hoshi Y, et al. Impaired Interhemispheric Integration inBrain Oxygenation and Hemodynamics in Schizophrenia[J]. European Archives ofPsychiatry and Clinical Neuroscience, 1994, 244(1):17–25.
102 Hirth C, Obrig H, Valdueza J, et al. Simultaneous Assessment of Cerebral Oxy-genation and Hemodynamics During a Motor Task: A Combined Near Infrared andTranscranial Doppler Sonography Study[J]. Advances in Experimental Medicineand Biology, 1997, 411:461–469.
103 Colier W N, Quaresima V, Oeseburg B, et al. Human Motor-cortex OxygenationChanges Induced by Cyclic Coupled Movements of Hand and Foot[J]. Experimen-tal Brain Research, 1999, 129(3):457–461.
104 Toronov V, Franceschini M A, Filiaci M, et al. Near-infrared Study of Fluctua-tions in Cerebral Hemodynamics During Rest and Motor Stimulation: TemporalAnalysis and Spatial Mapping[J]. Medical Physics, 2000, 27(4):801–815.
105 Franceschini M A, Toronov V, Filiaci M, et al. On-line Optical Imaging of theHuman Brain with 160-ms Temporal Resolution[J]. Optics Express, 2000, 6(3):49–57.
106 Heekeren H R, Obrig H, Wenzel R, et al. Cerebral Haemoglobin Oxygenation Dur-ing Sustained Visual Stimulation-a Near-infrared Spectroscopy Study[J]. Philo-sophical Transactions of the Royal Society of London Series B: Biological Sci-ences, 1977, 352:743–750.
107 Ruben J, Wenzel R, Obrig H, et al. Haemoglobin Oxygenation Changes DuringVisual Stimulation in the Occipital Cortex[J]. Advances in Cirrhosis, Hyperam-monemia, and Hepatic Encephalopathy, 1977, 428:181–187.
108 Sakatani K, Chen S, Lichty W, et al. Cerebral Blood Oxygenation Changes In-duced by Auditory Stimulation in Newborn Infants Measured by Near InfraredSpectroscopy[J]. Early Human Development, 1999, 55:229–236.
109 Zhang Y H, Brooks D H, Franceschini M A, et al. Eigenvector-based Spatial Fil-tering for Reduction of Physiological Interference in Diffuse Optical Imaging[J].Journal of Biomedical Optics, 2005, 10(1):011014.
110 Zhang Q, Brown E N, Strangman G E. Adaptive Filtering for Global InterferenceCancellation and Real-time Recovery of Evoked Brain Activity: A Monte CarloSimulation Study[J]. Journal of Biomedical Optics, 2007, 12(4):044014.
111 Zhang Q, Brown E N, Strangman G E. Adaptive Fitering to Reduce Global In-terference in Evoked Brain Activity Detection: A Human Subject Case Study[J].Journal of Biomedical Optics, 2007, 12(6):064009.
112 Obrig H, Neufang M, Wenzel R, et al. Spontaneous Low Frequency Oscillations ofCerebral Hemodynamics and Metabolism in Human Adults[J]. Neuroimage, 2000,12(6):623–639.
113 Cormick P W, Stewart M, Goetting M G, et al. Noninvasive Cerebral Optical Spec-troscopy for Monitoring Cerebral Oxygen Delivery and Hemodynamics[J]. CriticalCare Medicine, 1991, 19(1):89–97.
114 Kohl-Bareis M, Obrig H, Steinbrink K, et al. Noninvasive Monitoring of CerebralBlood Flow by a Dye Bolus Method: Separation of Brain from Skin and SkullSignals[J]. Journal of Biomedical Optics, 2002, 7(3):464–470.
115 Durduran T. Non-invasive Measurements of Tissue Hemodynamics with HybridDiffuse Optical Methods[D]. Pennsylvania:Ph.D Dissertion of Physics and Astron-omy in University of Pennsylvania, 2004:1–252.
116 Joseph D K, Huppert T J, Franceschini M A, et al. Diffuse Optical Tomogra-phy System to Image Brain Activation with Improved Spatial Resolution and Val-idation with Functional Magnetic Resonance Imaging[J]. Applied Optics, 2006,45(31):8142–8151.
117 Boas D A, Dale A M, Franceschini M A. Diffuse Optical Imaging of Brain Acti-vation: Approaches to Optimizing Image Sensitivity, Resolution, and Accuracy[J].NeuroImage, 2004, 23:S275–S288.
118 Jasdzewski G, Strangman G, Wagner J, et al. Differences in the Hemodynamic Re-sponse to Event-related Motor and Visual Paradigms as Measured by Near InfraredSpectroscopy[J]. NeuroImage, 2003, 20(1):479–488.
119 Allen M S. Models and Algorithms to Determine Cerebral Activation Using NearInfrared Spectroscopy[D]. Pennsylvania:Ph.D Dissertion of University of Texas,2006:1–240.
120 Zhang Q, Strangman G E, Ganis G. Adaptive Filtering to Reduce Global Inter-ference in Non-invasive Nirs Measures of Brain Activation: How Well and WhenDoes It Work?[J]. NeuroImage, 2009, 45:788–794.
121 Morren G, Wolf U, Lemmerling P, et al. Detection of Fast Neuronal Signals in theMotor Cortex from Functional Near Infrared Spectroscopy Measurements UsingIndependent Component Analysis[J]. Medical & Biological Engineering & Com-puting, 2004, 42(1):92–99.
122 Franceschini M A, Joseph D K, Huppert T J, et al. Diffuse Optical Imaging of theWhole Head[J]. Journal of Biomedical Optics, 2006, 11(5):054007.
123 Virtanen J, Noponen T, Merila¨inen P. Comparison of Principal and IndependentComponent Analysis in Removing Extracerebral Interference from Near-infraredSpectroscopy Signals[J]. Journal of Biomedical Optics, 2009, 14:054032.
124 Prince S, Kolehmainen V, Kaipio J P, et al. Time-series Estimation of BiologicalFactors in Optical Diffusion Tomography[J]. Physics in Medicine and Biology,2003, 48:1491–1504.
125 Diamond S G, Huppert T J, Kolehmainen V, et al. Physiological System Identifi-cation with the Kalman Filter in Diffuse Optical Tomography[J]. Medical ImageComputing and Computer-Assisted Intervention, 2005, 8:649–656.
126 Abdelnour A F, Huppert T. Real-time Imaging of Human Brain Function by Near-infrared Spectroscopy Using an Adaptive General Linear Model[J]. NeuroImage,2009, 46:133–143.
127季忠,秦树人.微弱生物医学信号特征提取的原理与实现[M].北京:科学出版社,2007:29–157.
128 Luu S, Chau T. Decoding Subjective Preference from Single-trial Near-infraredSpectroscopy Signals[J]. Journal of Neural Engineering, 2009, 6(1):1–8.
129 Yamada T, Umeyama S, Matsuda K. Multidistance Probe Arrangement to Elimi-nate Artifacts in Functional Near-infrared Spectroscopy[J]. Journal of BiomedicalOptics, 2009, 14(6):064034.
130周振宇,杨宏宇,龚辉,等.基于希尔伯特–黄变换的近红外脑功能成像信号分析[J].光学学报,2007,27(2):307–312.
131 Dam J S. Optical Analysis of Biological Media-continuous Wave Diffuse Spec-troscopy[D]. Lund:Ph.D Dissertion of Department of Physics in Lund Institute ofTechnology, 2000:37–80.
132 M. H.尼姆兹著,张镇西译.激光与生物组织的相互作用原理及应用[M].北京:科学出版社,2005:8–37.
133 Friebel M, Helfmann J, Netz U, et al. In?uence of Oxygen Saturation on the OpticalScattering Properties of Human Red Cells in the Spectral Range 250 to 2000 Nm[J].Journal of Biomedical Optics, 2009, 14(3):034001.
134 Van der Zee P. Measurement and Modelling of the Optical Properties of HumanTissue in the Near Infrared[D]. Lund:Ph.D Dissertion of University College Lon-don, 1992:1–297.
135罗荣辉,郭茂田.生物医学光子学[M].吉林:吉林大学出版社, 2008:6–433.
136 Furutsu K. Diffusion Equation Derived from Space-time Transport Equation[J].Journal of the Optical Society of America A: Optics, 1980, 70(4):360–366.
137 Patterson M S, Chance B, Wilson B C. Time Resolved Re?ectance and Transmit-tance for the Non-invasive Measurement of Tissue Optical Properties[J]. AppliedOptics, 1989, 28(12):2331–2336.
138蒋景英.人体内成分无创光谱检测中测量条件的研究[D].天津:天津大学生物医学工程学科博士学位论文,2002:10–12.
139刘玉良.动态光谱法血液成分无创检测初步研究[D].天津:天津大学生物医学工程学科博士学位论文,2006:35–37.
140 Wilson B C, Adam G. A Monte Carlo Model for the Absorption and Flux Distri-butions of Light in Tissue[J]. Medical Physics, 1983, 10:824–830.
141 Skipetrov S E, Chesnokov S S. Analysis, by the Monte Carlo Method, of theValidity of the Diffusion Approximation in a Study of Dynamic Multiple Scatter-ing of Light in Randomly Inhomogeneous Media[J]. Quantum Electronics, 1998,28(8):733–737.
142 Boas D A, Culver J P, Stott J J, et al. Three Dimensional Monte Carlo Code for Pho-ton Migration Through Complex Heterogeneous Media Including the Adult HumanHead[J]. Optics Express, 2002, 10(3):159–170.
143 Liu Q, Ramanujam N. Scaling Method for Fast Monte Carlo Simulation of DiffuseRe?ectance Spectra from Multilayered Turbid Media[J]. Journal of the OpticalSociety of America A: Optics, 2007, 24(4):1011–1025.
144 Alerstam E, Andersson-Engels S, Svensson T. White Monte Carlo for Time-resolved Photon Migration[J]. Journal of Biomedical Optics, 2008, 13(4):041304.
145 Tarvainen T, Vauhkonen M, Kolehmainen V, et al. Hybrid Radiative-transfer-diffusion Model for Optical Tomography[J]. Applied Optics, 2005, 44(6):876–886.
146 Guo X X, Wood M F G, Vitkin A. A Monte Carlo Study of Penetration Depth andSampling Volume of Polarized Light in Turbid Media[J]. Optics Communications,2008, 281:380–387.
147 Fukui Y, Ajichi Y, Okada E. Monte Carlo Prediction of Near-infrared Light Prop-agation in Realistic Adult and Neonatal Head Models[J]. Applied Optics, 2003,42(16):2881–2887.
148 Liu Q, Ramanujam N. Sequential Estimation of Optical Properties of a Two-layered Epithelial Tissue Model from Depth-resolved Ultraviolet-visible DiffuseRe?ectance Spectra[J]. Applied Optics, 2006, 45(19):4776–4790.
149 Farrell T J, Patterson M S, Essenpreis M. In?uence of Layered Tissue Architectureon Estimates of Tissue Optical Properties Obtained from Spatially Resolved DiffuseRe?ectometry[J]. Applied Optics, 1998, 37(10):1958–1972.
150 Alexandrakis G, Farrell T J, Patterson M S. Accuracy of the Diffusion Approx-imation in Determining the Optical Properties of a Two-layer Turbid Medium[J].Applied Optics, 1998, 37(31):7401–7409.
151 Wang L H, Jacques S L, Zheng L Q. Conv-convolution for Responses to a FiniteDiameter Photon Beam Incident on Multi-layered Tissues[J]. Computer Methodsand Programs in Biomedicine, 1997, 54:141–150.
152 Fabbri F, Sassaroli A, Henry M E, et al. Optical Measurements of AbsorptionChanges in Two-layered Diffusive Media[J]. Physics in Medicine and Biology,2004, 49:1183–1201.
153丁海曙,王峰,苏畅,等.近红外光子在生物组织中迁移的仿真及应用[J].清华大学学报(自然科学版),1999,39(9):5–8.
154骆清铭,龚辉,刘贤德,等.生物组织中激光传输规律的模拟与检验[J].光子学报,1995,24(2):125–129.
155 Arnold M L, Neill F E, Prestwich W V, et al. System Design for in Vivo NeutronActivation Analysis Measurements of Manganese in the Human Brain: Based onMonte Carlo Modeling[J]. Applied Radiation and Isotopes, 2000, 53(4-5):651–656.
156田会娟.用monte Carlo方法研究光源附近的空间分辨漫反射[D].天津:天津大学理学院硕士学位论文,2005:16–26.
157 Delpy D T, Cope M, Van der Zee P, et al. Estimation of Optical Pathlength ThroughTissue from Direct Time of Flight Measurements[J]. Physics in medicine and biol-ogy, 1988, 33(12):1433–1442.
158 Duncan A, Meek J H, Clemence M, et al. Optical Pathlength Measurementson Adult Head, Calf and Forearm and the Head of the Newborn Infant UsingPhase Resolved Optical Spectroscopy[J]. Physics in Medicine and Biology, 1995,40(2):295–304.
159 Van der Zee P, Cope M, Arridge S R, et al. Experimentally Measured Optical Path-lengths for the Adult Head, Calf and Forearm and the Head of the Newborn Infantas a Function of Inter Optode Spacing[J]. Advances in Experimental Medicine andBiology, 1992, 316:143–153.
160 Sayli O¨, Aksel E B, Akin A. Crosstalk and Error Analysis of Fat Layer on Contin-uous Wave Near-infrared Spectroscopy Measurements[J]. Journal of BiomedicalOptics, 2008, 13(6):064019.
161 Okui N, Okada E. Wavelength Dependence of Crosstalk in Dual Wavelength Mea-surement of Oxy- and Deoxy-hemoglobin[J]. Journal of Biomedical Optics, 2005,10(1):011015.
162刘铭.新生儿脑血氧监护仪的研制[D].天津:天津大学生物医学工程硕士论文,2005:1–20.
163 Franceschini M A, Fantini S, Thompson J H, et al. Hemodynamic Evoked Responseof the Sensorimotor Cortex Measured Noninvasively with Near-infrared OpticalImaging[J]. Psychophysiology, 2003, 40(4):548–560.
164 Huang N E, Shen Z, Long S R, et al. The Empirical Mode Decompositionand the Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analy-sis[C]//Proceedings of the Royal Society of London. Series A: Mathematical, Phys-ical and Engineering Sciences. 1998:903–995.
165 Cohen L. Time-frequency Distribution: A Review[C]//Proceedings of the IEEE.1989:941–981.
166王宏禹.信号处理相关理论综合与统一法[M].北京:国防工业出版社,2005:208–250.
167 Liang H, Lin Q H, Chen J D Z. Application of the Empirical Mode Decompositionto the Analysis of Esophageal Manometric Data in Gastroesophageal Re?ux Dis-ease[J]. IEEE Transactions on Biomedical Engineering, 2005, 52(10):1692–1701.
168 Saager R, Berger A. Measurement of Layer-like Hemodynamic Trends in Scalpand Cortex: Implications for Physiological Baseline Suppression in Fuctional Near-infrared Spectroscopy[J]. Journal of Biomedical Optics, 2008, 13(3):034017.
169 Van der Zee P, Essenpreis M, Delpy D T. Optical Properties of Brain Tis-sue[C]//Proceedings of SPIE. 1993:454–465.
170 Okada E, Delpy D T. Near-infrared Light Propagation in an Adult Head Model.Ii. Effect of Superficial Tissue Thickness on the Sensitivity of the Near-infraredSpectroscopy Signal[J]. Applied Optics, 2003, 42(16):2915–2922.
171 Strangman G, Franceschini M A, Boas D A. Factors Affecting the Accurary ofNear-infrared Spectroscopy Concentration Calculations for Focal Changes in Oxy-genation Parameters[J]. Neuroimage, 2003, 18(4):865–879.
172 Choi J, Wolf M, Toronov V, et al. Noninvasive Determination of the Optical Proper-ties of Adult Brain: Near-infrared Spectroscopy Approach[J]. Journal of Biomedi-cal Optics, 2004, 9(1):221–229.
173 Leung T S, Elwell C E, Delpy D T. Estimation of Cerebral Oxy- and Deoxy-haemoglobin Concentration Changes in a Layered Adult Head Model Using Near-infrared Spectroscopy and Multivariate Statistical Analysis[J]. Physics in Medicineand Biology, 2005, 50(24):5783–5798.
174 Kohri S, Hoshi Y, Tamura M, et al. Quantitative Evaluation of the Relative Contri-bution Ratio of Cerebral Tissue to Near-infrared Signals in the Adult Human Head:A Preliminary Study[J]. Physiological Measurement, 2002, 23(2):301–312.
175 Okada E, Delpy D T. Near-infrared Light Propagation in an Adult Head Model.I. Modeling of Low-level Scattering in the Cerebrospinal Fluid Layer[J]. AppliedOptics, 2003, 42(16):2906–2914.
176 Cohen M S. Real-time Functional Magnetic Resonance Imaging[J]. Methods,2001, 25:201–220.
177 Hiraoka M, Firbank M, Essenpreis M, et al. A Monte Carlo Investigation of Opti-cal Pathlength in Inhomogeneous Tissue and its Application to Near-infrared Spec-troscopy[J]. Physics in Medicine and Biology, 1993, 38:1859–1876.
178 Balocchi R, Menicucci D, Santarcangelo E, et al. Deriving the Respiratory SinusArrhythmia from the Heartbeat Time Series Using Empirical Mode Decomposi-tion[J]. Chaos, Solitons and Fractals, 2004, 20:171–177.
179 Durduran T, Yu G Q, Burnett M G. Diffuse Optical Measurement of Blood Flow,Blood Oxygenation, and Metabolism in a Human Brain During Sensorimotor Cor-tex Activation[J]. Optics Letters, 2004, 29(15):1766–1768.
180 Kocsis L, Herman P, Eke A. The Modified Beer-lambert Law Revisited[J]. Physicsin Medicine and Biology, 2006, 51:N91–N98.
181 Huppert T J, Diamond S G, Franceschini M A, et al. Homer: A Review of Time-series Analysis Methods for Near-infrared Spectroscopy of the Brain[J]. AppliedOptics, 2009, 48(10):D280–D298.
182 Uludag K, Kohl M, Steinbrink J, et al. Cross Talk in the Lambert-beer Calculationfor Near-infrared Wavelengths Estimated by Monte Carlo Simulations[J]. Journalof Biomedical Optics, 2002, 7(1):51–59.
183 Liang H, Lin Z, McCallum R W. Articfact Reduction in Electrogastrogram Basedon Empirical Mode Decomposition Method[J]. Medical & Biological Engineering& Computing, 2000, 38:35–41.
184 Qin S R, Zhong Y M. A New Envelope Algorithm of Hilbert-huang Transform[J].Mechanical Systems and Signal Processing, 2006, 20:1941–1952.
185 Toronov V, Webb A, Choi J H, et al. Study of Local Cerebral Hemodynamicsby Frequency-domain Near-infrared Spectroscopy and Correlation with Simultane-ously Acquired Functional Magnetic Resonance Imaging[J]. Optics Express, 2001,9(8):417–427.
186 Widrow B, Steams S D著,王永德等译.自适应信号处理[M].北京:机械工业出版社,2008:1–300.
187龚耀寰.自适应滤波-时域白适应滤波和智能大线[M].北京:电子工业出版社,2003:10–400.
188 P. S. R. Diniz著,刘郁林等译.自适应滤波算法与实现(第二版)[M].北京:电子工业出版社,2004.
189 Gibson A P, Hebden J C, Arridge S R. Recent Advances in Diffuse Optical Imag-ing[J]. Physics in Medicine and Biology, 2005, 50(4):R1–R43.
190 Stothers L, Shadgan B, Macnab A. Urological Applications of Near Infrared Spec-troscopy[J]. The Canadian Journal of Urology, 2008, 15(6):4399–4409.
191张贤达.矩阵分析与应用[M].北京:清华大学出版社, 2004:297–320.
192 Elwell C E, Springett R, Hillman E, et al. Oscillations in Cerebral Haemodynam-ics. Implications for Functional Activation Studies[J]. Advances in ExperimentalMedicine and Biology, 1999, 471:57–65.
193 Mu¨ller T, Timmer J, Reinhard M, et al. Detection of Very Low-frequency Oscilla-tions of Cerebral Haemodynamics Is In?uenced by Data Detrending[J]. Medical &Biological Engineering & Computing, 2003, 41(1):69–74.
194 Scholkmann F, Spichtig S, Muehlemann T, et al. How to Detect and Reduce Move-ment Artifacts in Near-infrared Imaging Using Moving Standard Deviation andSpline Interpolation[J]. Physiological Measurement, 2010, 31:649–662.
195 Haykin S.著,郑宝玉等译.自适应滤波器原理[M].北京:电子工业出版社,2003:1–339.
196 Herna′ndez S E, Rodr′?guez V D, Pe′rez J, et al. Diffuse Re?ectance SpectroscopyCharacterization of Hemoglobin and Intralipid Solutions: In Votro Measurementswith Continuous Variation of Absorption and Scattering[J]. Journal of BiomedicalOptics, 2009, 14(3):034026.
197 Song X M, Pogue B W, Jiang S D, et al. Automated Region Detection Based onthe Contrast-to-noise Ratio in Near-infrared Tomography[J]. Applied Optics, 2004,43(5):1053–1062.
198 Li A, Kwong R, Cerussi A, et al. Method for Recovering Quantitative Broad-band Diffuse Optical Spectra from Layered Media[J]. Applied Optics, 2007,46(21):4828–4833.
199 Yang Y, Soyemi O O, Landry M R, et al. In?uence of a Fat Layer on the NearInfrared Spectra of Human Muscle: Quantitative Analysis Based on Two-layeredMonte Carlo Simulations and Phantom Experiments[J]. Optics Express, 2005,13(5):1570–1579.
200 Nishidate I, Sasaoka K, Yuasa T, et al. Visualizing of Skin Chromophore Concen-trations by Use of Rgb Images[J]. Optics Letters, 2008, 33(19):2263–2265.
201 Drakaki E, Psycharakis S, Makropoulou M, et al. Optical Properties and Chro-mophore Concentration Measurements in Tissue-like Phantoms[J]. Optics Com-munications, 2005, 254:40–51.
202 Van Staveren H J, Moes C J, van Marie J, et al. Light Scattering in Intralipid-10%in the Wavelength Range of 400-1100 nm[J]. Applied Optics, 1991, 30(31):4507–4514.
203 Cubeddu R, Pifferi A, Taroni P, et al. A Solid Tissue Phantom for Photon Migra-tion Studies[J]. Physics in Medicine and Biology, 1997, 42:1971–1979.
204吴欣.基于近红外光谱技术的脑血氧监测技术的研究[D].哈尔滨:哈尔滨工业大学硕士学位论文,2010:1–59.
205刘睿.基于近红外光的血氧浓度测量[D].哈尔滨:哈尔滨工业大学硕士学位论文,2007:1–50.
206田丰华,丁海曙,蔡志刚,等.利用近红外稳态光谱评估恒河猴皮瓣的血氧供应[J].科学通报,2002,47(16):1250–1255.
207 Vaithianathan T, Tullis I D C, Everdell N, et al. Design of a Portable Near InfraredSystem for Topographic Imaging of the Brain in Babies[J]. Review of ScientificInstruments, 2004, 75(10):3276–3283.
208 Sako T, Hamaoka T, Higuchi H, Y, et al. Validity of Nir Spectroscopy for Quan-titatively Measuring Muscle Oxidative Metabolic Rate in Exercise[J]. Journal ofApplied Physiology, 2001, 90:338–344.
209 Nishidate I, Aizu Y, Mishina H. Estimation of Melanin and Hemoglobin in SkinTissue Using Multiple Regression Analysis Aided by Monte Carlo Simulation[J].Journal of Biomedical Optics, 2004, 9(4):700–710.
210 Yu G, Durduran T, Lech G. Time-dependent Blood Flow and Oxygenation in Hu-man Skeletal Muscles Measured with Noninvasive Near-infrared Diffuse OpticalSpectroscopies[J]. Journal of Biomedical Optics, 2005, 10(2):024027.
211 Binder J R, Frost J A, Hammeke T A, et al. Human Temporal Lobe Activation bySpeech and Nonspeech Sounds[J]. Cerebral Cortex, 2000, 10(5):512–528.
212 Newman S D, Twieg D. Differences in Auditory Processing of Words and Pseu-dowords: An Fmri Study[J]. Human Brain Mapping, 2001, 14(1):39–47.
213 Towle V L, Bolan?os J, Suarez D, et al. The Spatial Location of Eeg Electrodes: Lo-cating the Best-fitting Sphere Relative to Cortical Anatomy[J]. Electroencephalog-raphy and Clinical Neurophysiology, 1993, 86(1):1–6.
214 American Electroencephalographic Society. Guidelines for Standard Electrode Po-sition Nomenclature[J]. Journal of Clinical Neurophysiology, 1994, 11:111–113.
2戴丽娟.脑组织参数近红外实时在位微创测量技术及其应用研究[D].南京:南京航空航天大学精密仪器与机械学科博士学位论文,2008:1–25.
3邓玉林,李勤.生物医学工程学[M].北京:科学出版社, 2007:80–104.
4张连顺.光与生物组织的相互作用及生物组织光学特性参数测量[D].天津:南开大学凝聚态物理学科博士学位论文,2003:1–9.
5 Wang L H. Monte Carlo Modeling of Light Transport in Multi-layered Tissues inStandard C[D]. Texas:Ph.D Dissertion of Texas A&M University, 1998:1–168.
6 Cerussi A E. Quantitative Frequency-domain Fluorescence Spectroscopy in Tissuesand Tissue-like Media[D]. Urbana-Champaign:Ph.D Dissertion of University ofIllinois at Urbana-Champaign, 1999:1–233.
7 Matcher S J, Cope M, Delpy D T. In Vivo Measurements of the Wavelength Depen-dence of Tissue-scattering Coefficients between 760 and 900 Nm Measured withTime-resolved Spectroscopy[J]. Applied Optics, 1997, 36(1):386–396.
8 Pe′ry E, Blondel W C P M, Thomas C. Monte Carlo Modeling of Multilayer Phan-toms with Multiple Fluorophores: Simulation Algorithm and Experimental Valida-tion[J]. Journal of Biomedical Optics, 2009, 14(2):024048.
9 Umeyama S, Yamada T. Monte Carlo Study of Global Interference Cancellation byMultidistance Measurement of Near-infrared Spectroscopy[J]. Journal of Biomed-ical Optics, 2009, 14(6):064025.
10腾轶超,叶大田,李岳,等.无损检测组织氧饱和度的近红外光学传感器的优化设计研究[J].光谱学与光谱分析,2008,28(4):953–957.
11刘蓉,谷筱玉,徐可欣.近红外光谱无创血糖测量中背景扣除方法的研究[J].光谱学与光谱分析,2008,28(8):1772–1775.
12李刚,刘玉良,林凌,等.利用统计处理方法提高动态光谱的检测精度[J].光谱学与光谱分析,2007,27(9):1669–1672.
13郑毅,骆清铭,刘谦,等.适于脑功能活动检测的便携式近红外光谱仪的研制[J].中国生物医学工程学报,2007,26(6):898–902.
14张连顺,张春平,王新宇,等.两层生物组织光学特性参数无损测量的模拟研究[J].发光学报,2003,24(1):56–59.
15戴丽娟,王慧南,钱志余,等.血液可见吸收光谱与血氧参数神经网络估算法[J].光谱学与光谱分析,2008,28(7):1468–1472.
16王晶,张镇西,徐正红,等.基于anep染料荧光光谱迁移的单波长心脏光学标测系统[J].光谱学与光谱分析,2008,28(3):617–620.
17张镇西.“生物医学光子学[J].
18姚翠萍,张镇西.激光与组织的相互作用[J].激光生物学报, 1999, 23(6):344–349.
19 Xu L Q, Li H, Xie S S. Theoretical Analysis of Backscattered Polarization Patternsof Turbid Media Containing Glucose[J]. Chinese Optics Letters, 2007, 5(2):102–104.
20 Jo¨bsis F F. Noninvasive Infrared Monitoring of Cerebral and Myocardial Oxy-gen Sufficiency and Circulatory Parameters[J]. Chinese Optics Letters, 1977,198(4323):1264–1267.
21曹传花.便携式近红外脑功能成像系统的性能测试[D].武汉:华中科技大学生物医学工程学科硕士学位论文,2006:1–10.
22黄岚.近红外组织氧绝对量检测的研究及应用[D].北京:清华大学生物医学工程学科博士学位论文,2004:1–89.
23 Tseng S, Grant A, Durkin A J. In Vivo Determination of Skin Near-infrared OpticalProperties Using Diffuse Optical Spectroscopy[J]. Journal of Biomedical Optics,2008, 13(1):014016.
24 Laurent C, Jbnsson B, Vegfors M, et al. Non-invasive Measurement of SystolicBlood Pressure on the Arm Utilising Photoplethysmography: Development of theMethodology[J]. Medical & Biological Engineering & Computing, 2005, 43:131–135.
25 Yang Y, Landry M R, Soyemi O O, et al. Simultaneous Correction of the In?uenceof Skin Color and Fat on Tissue Spectroscopy by Use of a Two-distance Fiber-opticProbe and Orthogonalization Technique[J]. Optics Letters, 2005, 30(17):2269–2271.
26 El-Desoky A E, Seifalian A, Cope M, et al. Changes in Tissue Oxygenation ofthe Porcine Liver Measured by Near-infrared Spectroscopy[J]. Liver TransplantSurgery, 1999, 5:219–226.
27 Ronald X X, Bo Q, Jimmy J M, et al. Development of a Handheld Near-infraredImager for Dynamic Characterization of in Vivo Biological Tissue Systems[J]. Ap-plied Optics, 2007, 46(30):7442–7451.
28 Cheng X F, Mao J, Bush R, et al. Breast Cancer Detection by Mapping HemoglobinConcentration and Oxygen Saturation[J]. Applied Optics, 2003, 42(31):6412–6421.
29 Rea P A, Crowe J, Wickramasinghe Y, et al. Non-invasive Optical Methods forthe Study of Cerebral Metabolism in the Human Newborn: A Technique for theFuture?[J]. Journal of Medical Engineering Technology, 1985, 9(4):160–166.
30段相林,郭炳冉,辜清.人体组织学与解剖学[M].北京:高等教育出版社,2006:262–297.
31赵军.新生儿大脑组织光学参数的无损检测[D].北京:清华大学生物医学工程学科博士学位论文,2004:1–13.
32邱一华,彭聿平.生理学[M].北京:科学出版社, 2004:1–174.
33 Kiening K L, Hartl R, Unterberg A W, et al. Brain Tissue Po2-monitoring inComatose Patients: Implications for Therapy[J]. Neurological Research, 1997,19(3):233–240.
34陈婷方.脑红蛋白神经保护功能及其机制的初步研究[D].北京:中国人民解放军军事医学科学院病理学与病理生理学学科博士学位论文,2008:1–10.
35 Hintz S R. Near-infrared Spectroscopy: Neonatal and Perinatal Applications[J].NeoReviews, 2001, 2:22–28.
36 Gopinath S P, Robertson C S, Grossman R G, et al. Near-infrared SpectroscopicLocalization of Intracranial Hematomas[J]. Journal of Neurosurgery, 1993, 79:43–47.
37 Steen R G, Kitagishi K, Morgan K. In Vivo Measurement of Tumor Blood Oxy-genation by Near-infrared Spectroscopy: Immediate Effects of Pentobarbital Over-dose of Carmustine Treatment[J]. Journal of Neuro-Oncology, 1994, 22:209–220.
38 Hull E L, Conover D L, Foster T H. Carbogeninduced Changes in Rat MammaryTumor Oxygenation Reported by Near-infrared Spectroscopy[J]. British Journal ofCancer, 1999, 79:1709–1716.
39 Gratton E, Toronov V, Wolf U, et al. Measurement of Brain Activity by Near-infrared Light[J]. Journal of Biomedical Optics, 2005, 10(1):011008.
40 Sato H, Kiguchi M, Kawaguchi F, et al. Practicality of Wavelength Selection to Im-prove Signal-to-noise Ratio in Near-infrared Spectroscopy[J]. NeuroImage, 2003,21:1554–1562.
41 Isobe K, Kusaka T, Nagano K, et al. Functional Imaging of the Brain in SedatedNewborn Infants Using Near Infrared Topography During Passive Knee Movemen-t[J]. Neuroscience Letters, 2001, 299:221–224.
42李鹏程,曾绍群,骆清铭.脑皮层功能活动与病理状态光学检测方法研究进展[J].中国生物医学工程学报,2008,27(2):164–168.
43 Yang Y, Soyemi O O, Scott P J, et al. Quantitative Measurement of Muscle Oxy-gen Saturation without In?uence from Skin and Fat Using Continuous-wave NearInfrared Spectroscopy[J]. Optics Express, 2007, 15(21):13715–13730.
44 Sevick E M, Chance B, Leigh J, et al. Quantitation of Time and Frequency Re-solved Optical Spectra for the Determination of Tissue Oxygenation[J]. AnalyticalBiochemistry, 1991, 195(2):330–351.
45赵军,丁海曙,阮曼奇,等.利用频域近红外光谱仪和磁共振谱仪测量骨骼肌能量代谢[J].光谱学与光谱分析,2005,25(6):861–865.
46龚辉,李成军,李婷,等.前额叶皮层工作记忆作用的近红外光学成像[J].中国科学,2007,37(增):110–117.
47 De Blasi R A, Ferrari M, Natali A, et al. Noninvasive Measurement of ForearmBlood Flow and Oxygen Consumption by Nearinfrared Spectroscopy[J]. Journalof Applied Physiology, 1994, 76(3):1388–1393.
48 Shiga T, Tanabe K, Nakase Y, et al. Development of a Portable Tissue OximeterUsing Near Infrared Spectroscopy[J]. Medical & Biological Engineering & Com-puting, 1995, 33(4):622–626.
49 Chance B, Dait M T, Zhang C, et al. Recovery from Exercise-induced Desaturationin the Quadriceps Muscles of Elite Competitive Rowers[J]. American Journal ofPhysiology, 1992, 262(3):C766–C775.
50 Colier W N, Meeuwsen I B, Degens H, et al. Determination of Oxygen Consump-tion in Muscle During Exercise Using Near Infrared Spectroscopy[J]. Acta Anaes-thesiologica Scandinavica, 1995, 39(S107):151–155.
51 Thavasothy M, Broadhead M, Elwell C, et al. A Comparison of Cerebral Oxygena-tion as Measured by the Niro 300 and the Invos 5100 Near-infrared Spectropho-tometers[J]. Anaesthesia, 2002, 57(10):999–1006.
52 Quaresima V, Homma S, Azuma K, et al. Calf and Shin Muscle OxygenationPatterns and Femoral Artery Blood Flow During Dynamic Plantar Flexion Exercisein Humans[J]. European Journal of Applied Physiology, 2001, 84(5):387–394.
53 Franceschini M A, Boas D A, Zourabian A, et al. Near-infrared Spiroximetry: Non-invasive Measurements of Venous Saturation in Piglets and Human Subjects[J].Journal of Applied Physiology, 2002, 92(1):372–384.
54 Hoshi Y. Functional Near-infrared Optical Imaging: Utility and Limitations inHuman Brain Mapping[J]. Psychophysiology, 2003, 40(4):511–520.
55 Kennan R P, Horovitz S G, Maki A, et al. Simultaneous Recording of Event-relatedAuditory Oddball Response Using Transcranial Near Infrared Optical Topographyand Surface Eeg[J]. Neuroimage, 2002, 16(3):587–592.
56 Wolf M, Wolf U, Choi J H, et al. Functional Frequency-domain Near-infraredSpectroscopy Detects Fast Neuronal Signal in the Motor Cortex[J]. Neuroimage,2002, 17(4):1868–1875.
57 Hebden J C, Gibson A, Yusof R M, et al. Three-dimensional Optical Tomogra-phy of the Premature Infant Brain[J]. Physics in Medicine and Biology, 2002,47(32):4155–4166.
58 Cubeddu R, Biscotti G, Pifferi A, et al. Dual-wavelength, 8-channel Time-resolvedOximetry for Functional Muscle Studies[C]//Proceedings of Asian Symposium onBiomedical Optics and Photomedicine. 2002:198–199.
59李岳,丁海曙,黄岚,等.近红外光谱方法在颌面外科皮瓣移植术后监测中的应用[J].光谱学与光谱分析,2005,25(3):377–380.
60王峰, 李炜,林方,等.用近红外光谱技术实现生物组织含氧量的无损检测[J].清华大学学报(自然科学版),1999,39(7):16–19.
61黄岚,田丰华,丁海曙,等.用近红外光谱对组织氧测量方法的研究[J].红外与毫米波学报,2003,22(5):379–383.
62腾轶超,丁海曙,黄岚,等.外层覆盖下组织氧饱和度的无损检测及可信度评定[J].自然科学进展,2007,17(8):1138–1143.
63腾轶超,丁海曙,龚庆成,等.红外光谱监测体外循环手术中脑组织氧合状况的研究[J].光谱学与光谱分析,2006,26(5):828–832.
64丁海曙,腾轶超.组织血氧参数近红外无损检测技术及自主创新之路[J].激光与光电子学进展,2007,44(9):14–31.
65腾轶超.近红外空间分辨光谱技术及其在脑氧无损检测中的应用[D].北京:清华大学生物医学工程博士学位论文,2006:1–100.
66李婷,李黎,杜鹏,等.视觉非随意注意的近红外脑功能成像技术与事件相关脑电位检测技术研究[J].光学学报,2007,27(3):531–535.
67杨宏宇,周振宇,刘云,等.近红外光学成像技术检测视觉搜索任务期间大脑前额叶氧合血红蛋白水平变化[J].航天医学与医学工程,2007,20(3):209–212.
68吴太虎,徐可欣,刘庆珍,等.近红外光谱法无创测量人体血红蛋白浓度[J].激光生物学报,2006,15(2):204–208.
69相韶霞,林凌,王艳秋,等.近红外光谱组织血氧检测结果的定量化方法[J].光学技术,2001,27(5):451–455.
70赵会娟,阎长斐,张顺起,等.基于微扰蒙特卡罗的薄层状组织光学参数重构技术[J].纳米技术与精密工程,2009,7(3):254–258.
71王利军,刘迎,田会娟,等.利用微区空间分辨漫反射测量组织光学特性[J].光电子·激光,2009,20(1):122–125.
72林麟,林凌,李刚.一种新型算法在组织光学参数测量系统中的应用[J].北京生物医学工程,2007,26(1):84–86.
73万柏坤,刘延刚,明东,等.基于脑电特征的多模式想象动作识别[J].天津大学学报,2010,43(10):895–900.
74许棠.生物组织中的光传输及生物组织光学特性参数测量的研究[D].天津:南开大学凝聚态物理学科博士学位论文,2004:1–141.
75来建成.生物组织的光学描述与光传输规律研究[D].南京:南京理工大学光学工程学科博士学位论文,2005:1–103.
76陈卫国,李鹏程,骆清铭,等.用近红外光拓扑图技术短期预测脑梗塞[J].光子学报,2000,29(8):673–677.
77罗斌.漫射光成像理论模型及算法的性能研究[D].杭州:浙江大学光学工程学科博士学位论文,2007:1–112.
78李婷.光在三维结构组织中传输的monte Carlo模拟及脑功能成像研究[D].武汉:华中科技大学生物医学工程学科博士学位论文,2010:1–131.
79徐国栋,陈刚,周超彦,等.血氧含量的近红外测定及其在运动实践中的应用[J].武汉体育学院学报,2004,38(1):34–38.
80 Saager R B, Berger A J. Direct Characterization and Removal of Interfering Ab-sorption Trends in Two-layer Turbid Media[J]. Journal of the Optical Society ofAmerica A: Optics, 2005, 22(9):1874–1882.
81 Strangman G, Boas D A, Sutton J P. Non-invasive Neuroimaging Using Near-infrared Light[J]. Biological Psychiatry, 2002, 52:679–693.
82刘海龙.生物医学信号处理[M].北京:化学工业出版社, 2006:1–322.
83 Reite M, Zimmerman J E, Edrich J, et al. The Human Magnetoece- Phalogram:Some Eeg and Related Correlations[J]. Electroencephalography and Clinical Neu-rophysiology, 1976, 40(1):59–66.
84刘晓丹,黄力.脑磁图、磁源性成像在脑肿瘤中的研究进展[J].国际医学放射学杂志,2010,33(3):205–208.
85伍国锋,张文渊.脑电波产生的神经生理机制[J].临床脑电学杂志, 2000,9(3):188–190.
86胡洁,胡净,黄定君.脑磁图研究进展[J].生物医学工程与临床, 2003,7(3):181–184.
87 Robert L S. History and Future Directions of Human Brain Mapping and FunctionalNeuroimaging[J]. Acta Psychologica, 2001, 107(1-3):9–42.
88颜建华.正电子发射层析图像重建算法研究[D].武汉:华中科技大学微电子学与固体电子学学科博士学位论文,2007:1–12.
89庄华梅,何德.核磁共振技术及其在生命科学中的应用[J].生物磁学, 2005,5(4):59–61.
90 Matcher S J, Elwell C E, Cooper C E, et al. Performance Comparison of SeveralPublished Tissue Near-infrared Spectroscopy Algorithms[J]. Analytical Biochem-istry, 1995, 227:54–68.
91 Rolfe P. In Vivio Near-infrared Spectroscopy[J]. Annual Review of BiomedicalEngineering, 2000, 2:715–754.
92 Panerai R B. Assessment of Cerebral Pressure Autoregulation in Humans - a Re-view of Measurement Methods[J]. Physiological Measurement, 1998, 19(3):305–338.
93骆清铭.光电技术在生物医学中的应用–现状与发展[J].光学与光电技术,2003, 1(1):7–14.
94 Benaron D A, Hintz S R, Villringer A, et al. Noninvasive Functional Imaging ofHuman Brain Using Light[J]. Journal of Cerebral Blood Flow and Metabolism,2000, 20(3):469–477.
95 Toronov V, Webb A, Choi J H, et al. Investigation of Human Brain Hemodynamicsby Simultaneous Near-infrared Spectroscopy and Functional Magnetic ResonanceImaging[J]. Medical Physics, 2001, 28(4):521–527.
96李成军.近红外光谱技术用于前额叶皮层工作记忆作用的研究[D].武汉:华中科技大学生物医学工程学科博士学位论文,2005:1–83.
97 Kohno S, Miyai I, Seiyama A, et al. Removal of the Skin Blood Flow Artifact inFunctional Near-infrared Spectroscopic Imaging Data Through Independent Com-ponent Analysis[J]. Journal of Biomedical Optics, 2007, 12(6):062111.
98 Churchland P S, Sejnowski T J. Perspectives on Cognitive Neuroscience[J]. Sci-ence, 1988, 242:741–745.
99 Hoshi Y, Tamura M. Detection of Dynamic Changes in Cerebral Oxygenation Cou-pled to Neuronal Function During Mental Work in Man[J]. Neuroscience Letters,1993, 150(1):5–8.
100 Kato T, Kamei A, Takahashi S, et al. Human Visual Cortical Function During Pho-tonic Stimulation Monitoring by Means of Near-infrared Spectroscopy[J]. Journalof Cerebral Blood Flow and Metabolism, 1993, 13:516–520.
101 Okada F, Tokumitsu Y, Hoshi Y, et al. Impaired Interhemispheric Integration inBrain Oxygenation and Hemodynamics in Schizophrenia[J]. European Archives ofPsychiatry and Clinical Neuroscience, 1994, 244(1):17–25.
102 Hirth C, Obrig H, Valdueza J, et al. Simultaneous Assessment of Cerebral Oxy-genation and Hemodynamics During a Motor Task: A Combined Near Infrared andTranscranial Doppler Sonography Study[J]. Advances in Experimental Medicineand Biology, 1997, 411:461–469.
103 Colier W N, Quaresima V, Oeseburg B, et al. Human Motor-cortex OxygenationChanges Induced by Cyclic Coupled Movements of Hand and Foot[J]. Experimen-tal Brain Research, 1999, 129(3):457–461.
104 Toronov V, Franceschini M A, Filiaci M, et al. Near-infrared Study of Fluctua-tions in Cerebral Hemodynamics During Rest and Motor Stimulation: TemporalAnalysis and Spatial Mapping[J]. Medical Physics, 2000, 27(4):801–815.
105 Franceschini M A, Toronov V, Filiaci M, et al. On-line Optical Imaging of theHuman Brain with 160-ms Temporal Resolution[J]. Optics Express, 2000, 6(3):49–57.
106 Heekeren H R, Obrig H, Wenzel R, et al. Cerebral Haemoglobin Oxygenation Dur-ing Sustained Visual Stimulation-a Near-infrared Spectroscopy Study[J]. Philo-sophical Transactions of the Royal Society of London Series B: Biological Sci-ences, 1977, 352:743–750.
107 Ruben J, Wenzel R, Obrig H, et al. Haemoglobin Oxygenation Changes DuringVisual Stimulation in the Occipital Cortex[J]. Advances in Cirrhosis, Hyperam-monemia, and Hepatic Encephalopathy, 1977, 428:181–187.
108 Sakatani K, Chen S, Lichty W, et al. Cerebral Blood Oxygenation Changes In-duced by Auditory Stimulation in Newborn Infants Measured by Near InfraredSpectroscopy[J]. Early Human Development, 1999, 55:229–236.
109 Zhang Y H, Brooks D H, Franceschini M A, et al. Eigenvector-based Spatial Fil-tering for Reduction of Physiological Interference in Diffuse Optical Imaging[J].Journal of Biomedical Optics, 2005, 10(1):011014.
110 Zhang Q, Brown E N, Strangman G E. Adaptive Filtering for Global InterferenceCancellation and Real-time Recovery of Evoked Brain Activity: A Monte CarloSimulation Study[J]. Journal of Biomedical Optics, 2007, 12(4):044014.
111 Zhang Q, Brown E N, Strangman G E. Adaptive Fitering to Reduce Global In-terference in Evoked Brain Activity Detection: A Human Subject Case Study[J].Journal of Biomedical Optics, 2007, 12(6):064009.
112 Obrig H, Neufang M, Wenzel R, et al. Spontaneous Low Frequency Oscillations ofCerebral Hemodynamics and Metabolism in Human Adults[J]. Neuroimage, 2000,12(6):623–639.
113 Cormick P W, Stewart M, Goetting M G, et al. Noninvasive Cerebral Optical Spec-troscopy for Monitoring Cerebral Oxygen Delivery and Hemodynamics[J]. CriticalCare Medicine, 1991, 19(1):89–97.
114 Kohl-Bareis M, Obrig H, Steinbrink K, et al. Noninvasive Monitoring of CerebralBlood Flow by a Dye Bolus Method: Separation of Brain from Skin and SkullSignals[J]. Journal of Biomedical Optics, 2002, 7(3):464–470.
115 Durduran T. Non-invasive Measurements of Tissue Hemodynamics with HybridDiffuse Optical Methods[D]. Pennsylvania:Ph.D Dissertion of Physics and Astron-omy in University of Pennsylvania, 2004:1–252.
116 Joseph D K, Huppert T J, Franceschini M A, et al. Diffuse Optical Tomogra-phy System to Image Brain Activation with Improved Spatial Resolution and Val-idation with Functional Magnetic Resonance Imaging[J]. Applied Optics, 2006,45(31):8142–8151.
117 Boas D A, Dale A M, Franceschini M A. Diffuse Optical Imaging of Brain Acti-vation: Approaches to Optimizing Image Sensitivity, Resolution, and Accuracy[J].NeuroImage, 2004, 23:S275–S288.
118 Jasdzewski G, Strangman G, Wagner J, et al. Differences in the Hemodynamic Re-sponse to Event-related Motor and Visual Paradigms as Measured by Near InfraredSpectroscopy[J]. NeuroImage, 2003, 20(1):479–488.
119 Allen M S. Models and Algorithms to Determine Cerebral Activation Using NearInfrared Spectroscopy[D]. Pennsylvania:Ph.D Dissertion of University of Texas,2006:1–240.
120 Zhang Q, Strangman G E, Ganis G. Adaptive Filtering to Reduce Global Inter-ference in Non-invasive Nirs Measures of Brain Activation: How Well and WhenDoes It Work?[J]. NeuroImage, 2009, 45:788–794.
121 Morren G, Wolf U, Lemmerling P, et al. Detection of Fast Neuronal Signals in theMotor Cortex from Functional Near Infrared Spectroscopy Measurements UsingIndependent Component Analysis[J]. Medical & Biological Engineering & Com-puting, 2004, 42(1):92–99.
122 Franceschini M A, Joseph D K, Huppert T J, et al. Diffuse Optical Imaging of theWhole Head[J]. Journal of Biomedical Optics, 2006, 11(5):054007.
123 Virtanen J, Noponen T, Merila¨inen P. Comparison of Principal and IndependentComponent Analysis in Removing Extracerebral Interference from Near-infraredSpectroscopy Signals[J]. Journal of Biomedical Optics, 2009, 14:054032.
124 Prince S, Kolehmainen V, Kaipio J P, et al. Time-series Estimation of BiologicalFactors in Optical Diffusion Tomography[J]. Physics in Medicine and Biology,2003, 48:1491–1504.
125 Diamond S G, Huppert T J, Kolehmainen V, et al. Physiological System Identifi-cation with the Kalman Filter in Diffuse Optical Tomography[J]. Medical ImageComputing and Computer-Assisted Intervention, 2005, 8:649–656.
126 Abdelnour A F, Huppert T. Real-time Imaging of Human Brain Function by Near-infrared Spectroscopy Using an Adaptive General Linear Model[J]. NeuroImage,2009, 46:133–143.
127季忠,秦树人.微弱生物医学信号特征提取的原理与实现[M].北京:科学出版社,2007:29–157.
128 Luu S, Chau T. Decoding Subjective Preference from Single-trial Near-infraredSpectroscopy Signals[J]. Journal of Neural Engineering, 2009, 6(1):1–8.
129 Yamada T, Umeyama S, Matsuda K. Multidistance Probe Arrangement to Elimi-nate Artifacts in Functional Near-infrared Spectroscopy[J]. Journal of BiomedicalOptics, 2009, 14(6):064034.
130周振宇,杨宏宇,龚辉,等.基于希尔伯特–黄变换的近红外脑功能成像信号分析[J].光学学报,2007,27(2):307–312.
131 Dam J S. Optical Analysis of Biological Media-continuous Wave Diffuse Spec-troscopy[D]. Lund:Ph.D Dissertion of Department of Physics in Lund Institute ofTechnology, 2000:37–80.
132 M. H.尼姆兹著,张镇西译.激光与生物组织的相互作用原理及应用[M].北京:科学出版社,2005:8–37.
133 Friebel M, Helfmann J, Netz U, et al. In?uence of Oxygen Saturation on the OpticalScattering Properties of Human Red Cells in the Spectral Range 250 to 2000 Nm[J].Journal of Biomedical Optics, 2009, 14(3):034001.
134 Van der Zee P. Measurement and Modelling of the Optical Properties of HumanTissue in the Near Infrared[D]. Lund:Ph.D Dissertion of University College Lon-don, 1992:1–297.
135罗荣辉,郭茂田.生物医学光子学[M].吉林:吉林大学出版社, 2008:6–433.
136 Furutsu K. Diffusion Equation Derived from Space-time Transport Equation[J].Journal of the Optical Society of America A: Optics, 1980, 70(4):360–366.
137 Patterson M S, Chance B, Wilson B C. Time Resolved Re?ectance and Transmit-tance for the Non-invasive Measurement of Tissue Optical Properties[J]. AppliedOptics, 1989, 28(12):2331–2336.
138蒋景英.人体内成分无创光谱检测中测量条件的研究[D].天津:天津大学生物医学工程学科博士学位论文,2002:10–12.
139刘玉良.动态光谱法血液成分无创检测初步研究[D].天津:天津大学生物医学工程学科博士学位论文,2006:35–37.
140 Wilson B C, Adam G. A Monte Carlo Model for the Absorption and Flux Distri-butions of Light in Tissue[J]. Medical Physics, 1983, 10:824–830.
141 Skipetrov S E, Chesnokov S S. Analysis, by the Monte Carlo Method, of theValidity of the Diffusion Approximation in a Study of Dynamic Multiple Scatter-ing of Light in Randomly Inhomogeneous Media[J]. Quantum Electronics, 1998,28(8):733–737.
142 Boas D A, Culver J P, Stott J J, et al. Three Dimensional Monte Carlo Code for Pho-ton Migration Through Complex Heterogeneous Media Including the Adult HumanHead[J]. Optics Express, 2002, 10(3):159–170.
143 Liu Q, Ramanujam N. Scaling Method for Fast Monte Carlo Simulation of DiffuseRe?ectance Spectra from Multilayered Turbid Media[J]. Journal of the OpticalSociety of America A: Optics, 2007, 24(4):1011–1025.
144 Alerstam E, Andersson-Engels S, Svensson T. White Monte Carlo for Time-resolved Photon Migration[J]. Journal of Biomedical Optics, 2008, 13(4):041304.
145 Tarvainen T, Vauhkonen M, Kolehmainen V, et al. Hybrid Radiative-transfer-diffusion Model for Optical Tomography[J]. Applied Optics, 2005, 44(6):876–886.
146 Guo X X, Wood M F G, Vitkin A. A Monte Carlo Study of Penetration Depth andSampling Volume of Polarized Light in Turbid Media[J]. Optics Communications,2008, 281:380–387.
147 Fukui Y, Ajichi Y, Okada E. Monte Carlo Prediction of Near-infrared Light Prop-agation in Realistic Adult and Neonatal Head Models[J]. Applied Optics, 2003,42(16):2881–2887.
148 Liu Q, Ramanujam N. Sequential Estimation of Optical Properties of a Two-layered Epithelial Tissue Model from Depth-resolved Ultraviolet-visible DiffuseRe?ectance Spectra[J]. Applied Optics, 2006, 45(19):4776–4790.
149 Farrell T J, Patterson M S, Essenpreis M. In?uence of Layered Tissue Architectureon Estimates of Tissue Optical Properties Obtained from Spatially Resolved DiffuseRe?ectometry[J]. Applied Optics, 1998, 37(10):1958–1972.
150 Alexandrakis G, Farrell T J, Patterson M S. Accuracy of the Diffusion Approx-imation in Determining the Optical Properties of a Two-layer Turbid Medium[J].Applied Optics, 1998, 37(31):7401–7409.
151 Wang L H, Jacques S L, Zheng L Q. Conv-convolution for Responses to a FiniteDiameter Photon Beam Incident on Multi-layered Tissues[J]. Computer Methodsand Programs in Biomedicine, 1997, 54:141–150.
152 Fabbri F, Sassaroli A, Henry M E, et al. Optical Measurements of AbsorptionChanges in Two-layered Diffusive Media[J]. Physics in Medicine and Biology,2004, 49:1183–1201.
153丁海曙,王峰,苏畅,等.近红外光子在生物组织中迁移的仿真及应用[J].清华大学学报(自然科学版),1999,39(9):5–8.
154骆清铭,龚辉,刘贤德,等.生物组织中激光传输规律的模拟与检验[J].光子学报,1995,24(2):125–129.
155 Arnold M L, Neill F E, Prestwich W V, et al. System Design for in Vivo NeutronActivation Analysis Measurements of Manganese in the Human Brain: Based onMonte Carlo Modeling[J]. Applied Radiation and Isotopes, 2000, 53(4-5):651–656.
156田会娟.用monte Carlo方法研究光源附近的空间分辨漫反射[D].天津:天津大学理学院硕士学位论文,2005:16–26.
157 Delpy D T, Cope M, Van der Zee P, et al. Estimation of Optical Pathlength ThroughTissue from Direct Time of Flight Measurements[J]. Physics in medicine and biol-ogy, 1988, 33(12):1433–1442.
158 Duncan A, Meek J H, Clemence M, et al. Optical Pathlength Measurementson Adult Head, Calf and Forearm and the Head of the Newborn Infant UsingPhase Resolved Optical Spectroscopy[J]. Physics in Medicine and Biology, 1995,40(2):295–304.
159 Van der Zee P, Cope M, Arridge S R, et al. Experimentally Measured Optical Path-lengths for the Adult Head, Calf and Forearm and the Head of the Newborn Infantas a Function of Inter Optode Spacing[J]. Advances in Experimental Medicine andBiology, 1992, 316:143–153.
160 Sayli O¨, Aksel E B, Akin A. Crosstalk and Error Analysis of Fat Layer on Contin-uous Wave Near-infrared Spectroscopy Measurements[J]. Journal of BiomedicalOptics, 2008, 13(6):064019.
161 Okui N, Okada E. Wavelength Dependence of Crosstalk in Dual Wavelength Mea-surement of Oxy- and Deoxy-hemoglobin[J]. Journal of Biomedical Optics, 2005,10(1):011015.
162刘铭.新生儿脑血氧监护仪的研制[D].天津:天津大学生物医学工程硕士论文,2005:1–20.
163 Franceschini M A, Fantini S, Thompson J H, et al. Hemodynamic Evoked Responseof the Sensorimotor Cortex Measured Noninvasively with Near-infrared OpticalImaging[J]. Psychophysiology, 2003, 40(4):548–560.
164 Huang N E, Shen Z, Long S R, et al. The Empirical Mode Decompositionand the Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analy-sis[C]//Proceedings of the Royal Society of London. Series A: Mathematical, Phys-ical and Engineering Sciences. 1998:903–995.
165 Cohen L. Time-frequency Distribution: A Review[C]//Proceedings of the IEEE.1989:941–981.
166王宏禹.信号处理相关理论综合与统一法[M].北京:国防工业出版社,2005:208–250.
167 Liang H, Lin Q H, Chen J D Z. Application of the Empirical Mode Decompositionto the Analysis of Esophageal Manometric Data in Gastroesophageal Re?ux Dis-ease[J]. IEEE Transactions on Biomedical Engineering, 2005, 52(10):1692–1701.
168 Saager R, Berger A. Measurement of Layer-like Hemodynamic Trends in Scalpand Cortex: Implications for Physiological Baseline Suppression in Fuctional Near-infrared Spectroscopy[J]. Journal of Biomedical Optics, 2008, 13(3):034017.
169 Van der Zee P, Essenpreis M, Delpy D T. Optical Properties of Brain Tis-sue[C]//Proceedings of SPIE. 1993:454–465.
170 Okada E, Delpy D T. Near-infrared Light Propagation in an Adult Head Model.Ii. Effect of Superficial Tissue Thickness on the Sensitivity of the Near-infraredSpectroscopy Signal[J]. Applied Optics, 2003, 42(16):2915–2922.
171 Strangman G, Franceschini M A, Boas D A. Factors Affecting the Accurary ofNear-infrared Spectroscopy Concentration Calculations for Focal Changes in Oxy-genation Parameters[J]. Neuroimage, 2003, 18(4):865–879.
172 Choi J, Wolf M, Toronov V, et al. Noninvasive Determination of the Optical Proper-ties of Adult Brain: Near-infrared Spectroscopy Approach[J]. Journal of Biomedi-cal Optics, 2004, 9(1):221–229.
173 Leung T S, Elwell C E, Delpy D T. Estimation of Cerebral Oxy- and Deoxy-haemoglobin Concentration Changes in a Layered Adult Head Model Using Near-infrared Spectroscopy and Multivariate Statistical Analysis[J]. Physics in Medicineand Biology, 2005, 50(24):5783–5798.
174 Kohri S, Hoshi Y, Tamura M, et al. Quantitative Evaluation of the Relative Contri-bution Ratio of Cerebral Tissue to Near-infrared Signals in the Adult Human Head:A Preliminary Study[J]. Physiological Measurement, 2002, 23(2):301–312.
175 Okada E, Delpy D T. Near-infrared Light Propagation in an Adult Head Model.I. Modeling of Low-level Scattering in the Cerebrospinal Fluid Layer[J]. AppliedOptics, 2003, 42(16):2906–2914.
176 Cohen M S. Real-time Functional Magnetic Resonance Imaging[J]. Methods,2001, 25:201–220.
177 Hiraoka M, Firbank M, Essenpreis M, et al. A Monte Carlo Investigation of Opti-cal Pathlength in Inhomogeneous Tissue and its Application to Near-infrared Spec-troscopy[J]. Physics in Medicine and Biology, 1993, 38:1859–1876.
178 Balocchi R, Menicucci D, Santarcangelo E, et al. Deriving the Respiratory SinusArrhythmia from the Heartbeat Time Series Using Empirical Mode Decomposi-tion[J]. Chaos, Solitons and Fractals, 2004, 20:171–177.
179 Durduran T, Yu G Q, Burnett M G. Diffuse Optical Measurement of Blood Flow,Blood Oxygenation, and Metabolism in a Human Brain During Sensorimotor Cor-tex Activation[J]. Optics Letters, 2004, 29(15):1766–1768.
180 Kocsis L, Herman P, Eke A. The Modified Beer-lambert Law Revisited[J]. Physicsin Medicine and Biology, 2006, 51:N91–N98.
181 Huppert T J, Diamond S G, Franceschini M A, et al. Homer: A Review of Time-series Analysis Methods for Near-infrared Spectroscopy of the Brain[J]. AppliedOptics, 2009, 48(10):D280–D298.
182 Uludag K, Kohl M, Steinbrink J, et al. Cross Talk in the Lambert-beer Calculationfor Near-infrared Wavelengths Estimated by Monte Carlo Simulations[J]. Journalof Biomedical Optics, 2002, 7(1):51–59.
183 Liang H, Lin Z, McCallum R W. Articfact Reduction in Electrogastrogram Basedon Empirical Mode Decomposition Method[J]. Medical & Biological Engineering& Computing, 2000, 38:35–41.
184 Qin S R, Zhong Y M. A New Envelope Algorithm of Hilbert-huang Transform[J].Mechanical Systems and Signal Processing, 2006, 20:1941–1952.
185 Toronov V, Webb A, Choi J H, et al. Study of Local Cerebral Hemodynamicsby Frequency-domain Near-infrared Spectroscopy and Correlation with Simultane-ously Acquired Functional Magnetic Resonance Imaging[J]. Optics Express, 2001,9(8):417–427.
186 Widrow B, Steams S D著,王永德等译.自适应信号处理[M].北京:机械工业出版社,2008:1–300.
187龚耀寰.自适应滤波-时域白适应滤波和智能大线[M].北京:电子工业出版社,2003:10–400.
188 P. S. R. Diniz著,刘郁林等译.自适应滤波算法与实现(第二版)[M].北京:电子工业出版社,2004.
189 Gibson A P, Hebden J C, Arridge S R. Recent Advances in Diffuse Optical Imag-ing[J]. Physics in Medicine and Biology, 2005, 50(4):R1–R43.
190 Stothers L, Shadgan B, Macnab A. Urological Applications of Near Infrared Spec-troscopy[J]. The Canadian Journal of Urology, 2008, 15(6):4399–4409.
191张贤达.矩阵分析与应用[M].北京:清华大学出版社, 2004:297–320.
192 Elwell C E, Springett R, Hillman E, et al. Oscillations in Cerebral Haemodynam-ics. Implications for Functional Activation Studies[J]. Advances in ExperimentalMedicine and Biology, 1999, 471:57–65.
193 Mu¨ller T, Timmer J, Reinhard M, et al. Detection of Very Low-frequency Oscilla-tions of Cerebral Haemodynamics Is In?uenced by Data Detrending[J]. Medical &Biological Engineering & Computing, 2003, 41(1):69–74.
194 Scholkmann F, Spichtig S, Muehlemann T, et al. How to Detect and Reduce Move-ment Artifacts in Near-infrared Imaging Using Moving Standard Deviation andSpline Interpolation[J]. Physiological Measurement, 2010, 31:649–662.
195 Haykin S.著,郑宝玉等译.自适应滤波器原理[M].北京:电子工业出版社,2003:1–339.
196 Herna′ndez S E, Rodr′?guez V D, Pe′rez J, et al. Diffuse Re?ectance SpectroscopyCharacterization of Hemoglobin and Intralipid Solutions: In Votro Measurementswith Continuous Variation of Absorption and Scattering[J]. Journal of BiomedicalOptics, 2009, 14(3):034026.
197 Song X M, Pogue B W, Jiang S D, et al. Automated Region Detection Based onthe Contrast-to-noise Ratio in Near-infrared Tomography[J]. Applied Optics, 2004,43(5):1053–1062.
198 Li A, Kwong R, Cerussi A, et al. Method for Recovering Quantitative Broad-band Diffuse Optical Spectra from Layered Media[J]. Applied Optics, 2007,46(21):4828–4833.
199 Yang Y, Soyemi O O, Landry M R, et al. In?uence of a Fat Layer on the NearInfrared Spectra of Human Muscle: Quantitative Analysis Based on Two-layeredMonte Carlo Simulations and Phantom Experiments[J]. Optics Express, 2005,13(5):1570–1579.
200 Nishidate I, Sasaoka K, Yuasa T, et al. Visualizing of Skin Chromophore Concen-trations by Use of Rgb Images[J]. Optics Letters, 2008, 33(19):2263–2265.
201 Drakaki E, Psycharakis S, Makropoulou M, et al. Optical Properties and Chro-mophore Concentration Measurements in Tissue-like Phantoms[J]. Optics Com-munications, 2005, 254:40–51.
202 Van Staveren H J, Moes C J, van Marie J, et al. Light Scattering in Intralipid-10%in the Wavelength Range of 400-1100 nm[J]. Applied Optics, 1991, 30(31):4507–4514.
203 Cubeddu R, Pifferi A, Taroni P, et al. A Solid Tissue Phantom for Photon Migra-tion Studies[J]. Physics in Medicine and Biology, 1997, 42:1971–1979.
204吴欣.基于近红外光谱技术的脑血氧监测技术的研究[D].哈尔滨:哈尔滨工业大学硕士学位论文,2010:1–59.
205刘睿.基于近红外光的血氧浓度测量[D].哈尔滨:哈尔滨工业大学硕士学位论文,2007:1–50.
206田丰华,丁海曙,蔡志刚,等.利用近红外稳态光谱评估恒河猴皮瓣的血氧供应[J].科学通报,2002,47(16):1250–1255.
207 Vaithianathan T, Tullis I D C, Everdell N, et al. Design of a Portable Near InfraredSystem for Topographic Imaging of the Brain in Babies[J]. Review of ScientificInstruments, 2004, 75(10):3276–3283.
208 Sako T, Hamaoka T, Higuchi H, Y, et al. Validity of Nir Spectroscopy for Quan-titatively Measuring Muscle Oxidative Metabolic Rate in Exercise[J]. Journal ofApplied Physiology, 2001, 90:338–344.
209 Nishidate I, Aizu Y, Mishina H. Estimation of Melanin and Hemoglobin in SkinTissue Using Multiple Regression Analysis Aided by Monte Carlo Simulation[J].Journal of Biomedical Optics, 2004, 9(4):700–710.
210 Yu G, Durduran T, Lech G. Time-dependent Blood Flow and Oxygenation in Hu-man Skeletal Muscles Measured with Noninvasive Near-infrared Diffuse OpticalSpectroscopies[J]. Journal of Biomedical Optics, 2005, 10(2):024027.
211 Binder J R, Frost J A, Hammeke T A, et al. Human Temporal Lobe Activation bySpeech and Nonspeech Sounds[J]. Cerebral Cortex, 2000, 10(5):512–528.
212 Newman S D, Twieg D. Differences in Auditory Processing of Words and Pseu-dowords: An Fmri Study[J]. Human Brain Mapping, 2001, 14(1):39–47.
213 Towle V L, Bolan?os J, Suarez D, et al. The Spatial Location of Eeg Electrodes: Lo-cating the Best-fitting Sphere Relative to Cortical Anatomy[J]. Electroencephalog-raphy and Clinical Neurophysiology, 1993, 86(1):1–6.
214 American Electroencephalographic Society. Guidelines for Standard Electrode Po-sition Nomenclature[J]. Journal of Clinical Neurophysiology, 1994, 11:111–113.