基于线性映射场的fNIRS信号特征提取与分析  

Feature Extraction and Analysis of fNIRS Signals Based on Linear Mapping Field

作　　者：姚宇轩 孙兆辉 高毓兵 吴奇[1] YAO Yuxuan;SUN Zhaohui;GAO Yubing;WU Qi(School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China;School of Mechanical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)

机构地区：[1]上海交通大学电子信息与电气工程学院,上海200240 [2]上海交通大学机械与动力工程学院,上海200240

出　　处：《电子与信息学报》2023年第4期1401-1411,共11页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(U1933125,62171274);空军医学科研重大项目(2021KHYX11);国防创新特区项目(193-CXCY-A04-01-11-03);上海市级科技重大专项(2021SHZDZX)。

摘　　要：大脑功能性激活的相关研究普遍存在特征提取依赖人工经验、深层次生理学信息难以挖掘两大问题。针对这两个问题,该文通过引入变分模态分解(VMD)技术,提出自适应VMD算法。该算法考虑了脑血氧信号在不同频段下的生理意义,降低了传统VMD对超参数选取的依赖。实验结果表明自适应VMD算法能够精确地提取出功能性近红外光谱(fNIRS)中富有生理学意义的有效模态分量,进而提升数据预处理效果。在此基础上,基于将时间序列映射成图像并使用深度卷积神经网络进行特征学习的思路,提出线性映射场(LMF)。基于LMF,该文以较低的运算量将fNIRS序列映射成2维图像,辅以深度卷积神经网络,实现了fNIRS生理信号深层次特征的提取。实验结果证明了所提出LMF的优势。最后,该文对提出方法的有效性进行了讨论与分析,说明了不同于循环神经网络仅能“顺序”地感知时间序列,卷积神经网络对时间序列的“跳跃”感知是其取得优异效果的关键。There are two major problems in the research on brain functional activation:feature extraction relies on experience;and it is difficult to mine deep physiological information.Focusing on these two problems,this paper proposes a self-adaptive Variational Mode Decomposition(VMD)algorithm by introducing the VMD technique.The algorithm considers the physiological significance of cerebral blood oxygen signals in different frequency bands and reduces the dependence on the selection of hyperparameters.The experimental results show that the self-adaptive VMD algorithm can accurately extract meaningful components in functional Near-InfraRed Spectroscopy(fNIRS),thereby improving the effect of preprocessing.Secondly,this paper proposes Linear Mapping Field(LMF)based on the idea of mapping time series into images and using deep convolutional neural networks for learning.Based on LMF,this paper maps the fNIRS sequence into a twodimensional image with a low amount of computation supplemented by a deep convolutional neural network,and realizes the extraction of deep features of fNIRS physiological signals.The experimental results demonstrate the performance and advantages of the proposed LMF.Finally,this paper discusses and analyzes the effectiveness of the proposed methods,indicating that different from recurrent neural networks which can only perceive the time series in a“sequential”manner,the convolutional neural networks’characteristic of"jumping"perception is the key to achieving excellent results.

关 键 词：脑功能性激活 功能性近红外光谱 特征提取 变分模态分解 生理信息 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]