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1. 国防科技大学机电工程与自动化学院,湖南,长沙,410073
2. 国防科技大学电子科学与工程学院,湖南,长沙,410073
3. 国防科技大学机电工程与自动化学院湖南长沙,410073
4. 国防科技大学电子科学与工程学院湖南长沙,410073
Published:2008
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HU De-wen, HUANG Xiao-bin, LIU Ya-dong, et al. The Low Frequency Vibration Analysis for the Background Maps and Its Application to the Detection of Neuronal Activity Maps[J]. Acta Electronica Sinica, 2008, 36(10): 2065-2069.
在脑光学功能成像领域
传统的比差法由于受到背景低频振荡信号的影响和算法参数选择的随意性
无法有效地消除背景干扰.本文在仔细研究强0.1Hz低频振荡在脑区域分布的前提下
提出了一种基于低频振荡的比差检测方法.该方法利用0.1Hz低频振荡特性进行跨周期比差
极大的消除了背景图像对激活模式图像的干扰
从而有效地提高了算法的检测性能和鲁棒性.此外研究中还发现
在某些刺激加载时刻
由于比差法和基于低频振荡的比差法受低频振荡相位的影响
当算法参数选择不当时
即使在高信噪比下也无法获取好的检测效果法.为此
本文在设计了一种新的像素点时间序列选择策略的基础上
成功地将非参数化的时间解相关盲源分离算法引入到脑光学功能成像领域
从而提高了检测性能.
In the analysis of optical imaging of functional brain
the traditional difference image(DI) method can not remove the vascular artifact effectively due to the existence of low frequency vibration and the wrong parameters set.In order to resolve this problem
we first study the distribution of the 0.1Hz low frequency vibration in the brain
then according to the vibration characteristic
present a new low frequency vibration difference image(LFVDI) method.The LFVDI method gets the differential maps with the help of the low frequency vibration characteristic
so it can remove the vascular artifact sharply
Compared to the DI method
the LFVDI method increases the detection performance and robustness greatly.Further
in our study
we find though in the high SNR
the DI and LFVDI methods can not detect the activity maps accurately because of the impact of phase of 0.1Hz low frequency vibration and the incorrect parameters set.So in this circumstance
we design a select strategy for the pix time series
and using this select strategy
the nonparametric temporal decorrelation source separation(TDSEP) algorithm is introduced into the optical imaging of functional brain successfully
the simulation results show that the detection performance can be improved greatly.
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