红外技术2024，Vol.46Issue(8)：923-932,10.
基于卷积自编码的fNIRS信号运动校正算法研究
fNIRS Signal Motion Correction Algorithm Based on Convolutional Self-Coding
摘要
Abstract
Functional near-infrared spectroscopy(fNIRS)has attracted considerable attention in recent years in brain neuroscience as a brain imaging system with high temporal resolution,low cost,and high portability.However,motion artifacts in fNIRS signals interfere with the results of subsequent data analysis,and the denoising effect of some existing algorithms is insufficient.Therefore,a motion artifact correction algorithm for fNIRS signals based on a multilayer convolutional self-coding(MCAN)algorithm is proposed.The algorithm was used to correct three motion artifacts in the fNIRS signals.Next,the performance of the proposed algorithm was verified using simulation and experimental data and compared with several widely used algorithms.The results show that the MCAN algorithm performs satisfactorily in the remaining number of motion pseudo-traces,mean squared error,signal-to-noise ratio,square of Pearson correlation coefficient,and peak-to-peak error.Therefore,the proposed algorithm can be used as an efficient fNIRS signal preprocessing algorithm.关键词
功能性近红外光谱/卷积自编码/卷积神经网络/预处理/运动伪迹Key words
functional near-infrared spectroscopy/convolutional autoencoder/convolutional neural networks/signal processing/motion artifacts分类
基础医学引用本文复制引用
李永康,李茜,王琦雯,徐琪,李晓欧..基于卷积自编码的fNIRS信号运动校正算法研究[J].红外技术,2024,46(8):923-932,10.基金项目
上海市科委地方院校能力建设项目(22010502400). （22010502400）
