Human gait is a complex activity that requires high coordination between the central nervous system, the limb, and the musculoskeletal system. More research is needed to understand the latter coordination's complexity in designing better and more effective rehabilitation strategies for gait disorders. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are among the most used technologies for monitoring brain activities due to portability, non-invasiveness, and relatively low cost compared to others. Fusing EEG and fNIRS is a well-known and established methodology proven to enhance brain–computer interface (BCI) performance in terms of classification accuracy, number of control commands, and response time. Although there has been significant research exploring hybrid BCI (hBCI) involving both EEG and fNIRS for different types of tasks and human activities, human gait remains still underinvestigated. In this article, we aim to shed light on the recent development in the analysis of human gait using a hybrid EEG-fNIRS-based BCI system. The current review has followed guidelines of preferred reporting items for systematic reviews and meta-Analyses (PRISMA) during the data collection and selection phase. In this review, we put a particular focus on the commonly used signal processing and machine learning algorithms, as well as survey the potential applications of gait analysis. We distill some of the critical findings of this survey as follows. First, hardware specifications and experimental paradigms should be carefully considered because of their direct impact on the quality of gait assessment. Second, since both modalities, EEG and fNIRS, are sensitive to motion artifacts, instrumental, and physiological noises, there is a quest for more robust and sophisticated signal processing algorithms. Third, hybrid temporal and spatial features, obtained by virtue of fusing EEG and fNIRS and associated with cortical activation, can help better identify the correlation between brain activation and gait. In conclusion, hBCI (EEG + fNIRS) system is not yet much explored for the lower limb due to its complexity compared to the higher limb. Existing BCI systems for gait monitoring tend to only focus on one modality. We foresee a vast potential in adopting hBCI in gait analysis. Imminent technical breakthroughs are expected using hybrid EEG-fNIRS-based BCI for gait to control assistive devices and Monitor neuro-plasticity in neuro-rehabilitation. However, although those hybrid systems perform well in a controlled experimental environment when it comes to adopting them as a certified medical device in real-life clinical applications, there is still a long way to go.

中文翻译：

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使用基于混合 EEG-fNIRS 的 BCI 系统分析人类步态：综述

人类步态是一项复杂的活动，需要中枢神经系统、四肢和肌肉骨骼系统之间的高度协调。需要更多的研究来了解后者在为步态障碍设计更好、更有效的康复策略方面的复杂性。与其他技术相比，脑电图 (EEG) 和功能性近红外光谱 (fNIRS) 是用于监测大脑活动的最常用技术之一，因为它具有便携性、非侵入性和相对较低的成本。融合 EEG 和 fNIRS 是一种众所周知的成熟方法，经证明可以在分类准确性、控制命令数量和响应时间方面提高脑机接口 (BCI) 性能。尽管已经有大量研究探索混合 BCI (hBCI)，涉及 EEG 和 fNIRS 用于不同类型的任务和人类活动，但人类步态仍未得到充分研究。在本文中，我们旨在阐明使用基于混合 EEG-fNIRS 的 BCI 系统分析人类步态的最新进展。本综述在数据收集和选择阶段遵循了系统评价和荟萃分析 (PRISMA) 的首选报告项目指南。在这篇综述中，我们特别关注常用的信号处理和机器学习算法，并调查步态分析的潜在应用。我们将本次调查的一些重要发现提炼如下。第一的，应仔细考虑硬件规格和实验范例，因为它们直接影响步态评估的质量。其次，由于 EEG 和 fNIRS 这两种模式对运动伪影、仪器和生理噪声都很敏感，因此需要更强大和更复杂的信号处理算法。第三，通过融合 EEG 和 fNIRS 获得并与皮质激活相关的混合时空特征可以帮助更好地识别大脑激活和步态之间的相关性。总之，与上肢相比，下肢的 hBCI (EEG + fNIRS) 系统由于其复杂性而尚未得到太多探索。现有的用于步态监测的 BCI 系统往往只关注一种模式。我们预见到在步态分析中采用 hBCI 的巨大潜力。预计使用基于混合 EEG-fNIRS 的 BCI 进行步态控制辅助设备和监测神经康复中的神经可塑性将取得迫在眉睫的技术突破。然而，尽管这些混合系统在实际临床应用中作为经过认证的医疗设备在受控实验环境中表现良好，但仍有很长的路要走。