基于改进薛定谔滤波的fNIRS信号伪迹去除算法
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沈阳航空航天大学电子信息工程学院

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TN911.7

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国家青年科学基金(61801308)、航空科学基金(2020Z017054001)、辽宁省教育厅一般项目(JYT2020049)、辽宁省青年科技人才“育苗”项目(JYT2020129)


Artifact removal algorithm for fNIRS signals based on improved schr?dinger filtering
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    摘要:

    功能性近红外光谱(functional Near-Infrared Spectroscopy,fNIRS)是一种新兴的光学神经成像技术,提供了无创、便携且低成本的脑活动监测方法。针对受试者头部移动产生的运动伪迹,结合数学形态学方法,提出了一种改进薛定谔滤波的fNIRS信号运动伪迹去除算法。将该算法分别应用于仿真和真实实验得到的反映受试者血红蛋白浓度变化情况的光密度信号,并与时间导数分布修复和峰度小波等伪迹去除算法进行性能对比。结果表明,所提算法可将未校正信号的信噪比提升28.66 dB、均方根误差降低到0.06、皮尔逊相关系数的平方提升到0.83、峰峰值误差降低到0.05,相对于其他算法更能有效地去除运动伪迹。

    Abstract:

    Functional Near-Infrared Spectroscopy (fNIRS) is an emerging optical neuroimaging technology that offers a non-invasive, portable, and cost-effective method for monitoring brain activity. Aiming at the motion artifacts caused by the subjects" head movement, an improved schr?dinger filter algorithm for removing motion artifacts from fNIRS signals was proposed in combination with mathematical morphology method. The algorithm was applied to the optical density signals obtained from simulation and real experiments, reflecting the changes in hemoglobin concentration of the subjects, and its performance was compared with artifact removal algorithms such as time derivative distribution repair and kurtosis wavelet. The results show that the proposed algorithm can improve the signal-to-noise ratio of the uncorrected signal by 28.66 dB, reduce the root mean square error to 0.06, increase the square of the Pearson correlation coefficient to 0.83, and reduce the peak-to-peak error to 0.05. Compared with other algorithms, it can remove motion artifacts more effectively.

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  • 收稿日期:2024-09-06
  • 最后修改日期:2024-10-29
  • 录用日期:2024-11-08
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