基于fNIRS信号的运动伪影去除算法综述
A Review of Motion Artifact Removal Algorithms Based on fNIRS Signals
DOI: 10.12677/sea.2025.145101, PDF,   
作者: 方书棋, 曹博奕, 董祥美, 高秀敏*：上海理工大学光电信息与计算机工程学院，上海；王 军, 李先茜：杨浦区精神卫生中心，上海；邢志明：临沂大学信息科学与工程学院，山东 临沂
关键词: 功能性近红外脑成像；运动伪影；信号预处理；滤波技术；血液动力学反应；噪声抑制；Functional Near-Infrared Brain Imaging； Motion Artifacts； Signal Preprocessing； Filtering Technology； Hemodynamic Response； Noise Suppression
摘要: 近红外脑功能成像技术(functional near-infrared spectroscopy, fNIRS)是一种新型、无创的脑功能检测技术。由于光源探测器与头皮之间的相对运动会引起信号的运动伪影，严重影响信号质量，这也成为了如今该技术的一个重要研究方向。经过多年的研究与发展，人们已经提出小波滤波、样条插值、主成分分析、基于相关的信号改进、深度学习等运动伪影去除技术。本文旨在系统地总结已有的运动伪影去除技术，并阐述了当前普遍使用的定量评价指标：均方误差(MSE)、皮尔逊相关性(R)、平均绝对百分比误差(MAPE)、曲线下面积(AUC)。最后总结了在运动伪影去除领域研究的不足，并提出展望。
Abstract: Functional near-infrared spectroscopy (fNIRS) is a new, non-invasive technique for detecting brain function. Because the relative motion between the light source detector and the scalp causes the motion artifact of the signal, the signal quality is seriously affected, which has become an important research direction of this technology. After years of research and development, people have proposed wavelet filtering, spline interpolation, principal component analysis, Deep learning, correlation based signal improvement and other motion artifact removal techniques. The purpose of this paper is to systematically summarize the existing motion artifact removal techniques, and describe the commonly used quantitative evaluation indexes: mean square error (MSE), Pearson correlation (R), mean absolute percentage error (MAPE), and area under the curve (AUC). Finally, the shortcomings of the research in the field of motion artifact removal are summarized, and the prospect is put forward.
文章引用：方书棋, 王军, 曹博奕, 李先茜, 邢志明, 董祥美, 高秀敏. 基于fNIRS信号的运动伪影去除算法综述[J]. 软件工程与应用, 2025, 14(5): 1138-1154. https://doi.org/10.12677/sea.2025.145101

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