基于不同敏感深度的近红外无创脑功能活动检测方法及脑力负荷评估研究

项目名称： 基于不同敏感深度的近红外无创脑功能活动检测方法及脑力负荷评估研究

项目编号： No.61201017

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 电子学与信息系统

项目作者： 张岩

作者单位： 哈尔滨工业大学

项目金额： 26万元

中文摘要： 基于近红外光谱技术的脑功能活动检测，对深入分析神经信息加工过程、揭示脑的高级功能以及理解脑疾病机理具有重大的科学意义和应用价值。人体生理干扰、运动伪迹、脑组织非层状特性、串扰问题增加了脑功能活动检测的难度，制约了应用领域的深入研究和拓展。针对以上问题，本项目以敏感深度与光源检测器间距相关为出发点，提出基于不同敏感深度的脑功能活动检测方法。利用光子传输模型实现光学成像传感探头的设计与优化，探索基于多距匹配的脑功能成像算法。研究脑组织非层状特性及生理干扰多成分特性等复杂问题对脑功能检测的影响，通过在体实验统计分析无效激活现象与非层状特性的关联性，并构建优化算法提高脑组织非均匀性严重情况下的脑活动信号提取精度。为揭示与知觉、认知和体力任务相联系的脑功能的变化和脑活动的水平，开展脑力负荷评估的前沿性应用研究。项目的研究将借助国际合作，多学科交叉，其创新性成果将全面推进功能近红外光谱技术的发展。

中文关键词： 功能近红外光谱技术；生理干扰；递归最小二乘；主成分分析；最小一乘算法

英文摘要： The measurement of brain activity with near infrared spectroscopy is of great scientific significance and application value for deeply analyzing the neural information processing course, revealing the higher brain function and understanding the brain disease mechanism. However, the physiological interference, motion artifact, layered heterogeneous characteristics, crosstalk increase the difficulty of the precise measurement of brain activity, and also restrict the further study and development of the application fields. In view of the above problems, this project presented the methodology for brain activity measurement based on different sensitive depth of the brain according to the correlation between the sensitive depth and the source-detector distance. The optical imaging probe will be designed and optimized by utilizing photon propagation model. The imaging algorithm of brain activity measurement will then be explored based on the multi-distance matching. The project will also carry out the research on the layered heterogeneity and multi-component characteristic of physiological interference, analyze the correlation between the "bad activator" and the layered heterogeneity, and improve the precision of the signal extraction for high heterogeneity by constructing optimization algorithm. For revealing the chan

英文关键词： functional near-infrared spectroscopy；physiological interference；recursive least squares；principle content analysis；Least Absolute Deviation