Huang Ruisen, Hong Keum-Shik, Yang Dalin, Huang Guanghao
School of Mechanical Engineering, Pusan National University, Busan, South Korea.
Department of Cogno-Mechatronics Engineering, Pusan National University, Busan, South Korea.
Front Neurosci. 2022 Oct 3;16:878750. doi: 10.3389/fnins.2022.878750. eCollection 2022.
With the emergence of an increasing number of functional near-infrared spectroscopy (fNIRS) devices, the significant deterioration in measurement caused by motion artifacts has become an essential research topic for fNIRS applications. However, a high requirement for mathematics and programming limits the number of related researches. Therefore, here we provide the first comprehensive review for motion artifact removal in fNIRS aiming to (i) summarize the latest achievements, (ii) present the significant solutions and evaluation metrics from the perspective of application and reproduction, and (iii) predict future topics in the field. The present review synthesizes information from fifty-one journal articles (screened according to three criteria). Three hardware-based solutions and nine algorithmic solutions are summarized, and their application requirements (compatible signal types, the availability for online applications, and limitations) and extensions are discussed. Five metrics for noise suppression and two metrics for signal distortion were synthesized to evaluate the motion artifact removal methods. Moreover, we highlight three deficiencies in the existing research: (i) The balance between the use of auxiliary hardware and that of an algorithmic solution is not clarified; (ii) few studies mention the filtering delay of the solutions, and (iii) the robustness and stability of the solution under extreme application conditions are not discussed.
随着越来越多的功能近红外光谱（fNIRS）设备的出现，由运动伪影导致的测量显著恶化已成为fNIRS应用的一个重要研究课题。然而，对数学和编程的高要求限制了相关研究的数量。因此，在此我们针对fNIRS中的运动伪影去除提供了首次全面综述，旨在（i）总结最新成果，（ii）从应用和重现的角度呈现重要的解决方案及评估指标，以及（iii）预测该领域未来的主题。本综述综合了来自五十一篇期刊文章的信息（根据三个标准筛选）。总结了三种基于硬件的解决方案和九种算法解决方案，并讨论了它们的应用要求（兼容信号类型、在线应用的可用性及局限性）和扩展。综合了五种噪声抑制指标和两种信号失真指标来评估运动伪影去除方法。此外，我们强调了现有研究中的三个不足：（i）辅助硬件使用与算法解决方案使用之间的平衡未阐明；（ii）很少有研究提及解决方案的滤波延迟，以及（iii）未讨论解决方案在极端应用条件下的鲁棒性和稳定性。
