Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review - Khan, Haroon; Naseer, Noman; Yazidi, Anis; Eide, Per Kristian; Hassan, Hafiz Wajahat; Mirtaheri, Peyman (Frontiers in Human Neuroscience, 2021)
Human gait is a complex activity that requires high coordination between the brain, central nervous system, and musculoskeletal system. In designing better and more effective rehabilitation strategies for gait disorders, more research is needed to understand the latter coordination's complexity. Fusing EEG and fNIRS is a well-known and established methodology proven to enhance brain-machine interface (BCI) performance in terms of classification accuracy, number of control commands, and response time. Although there has been significant research exploring hybrid brain-machine interface (hBCI) involving both EEG and fNIRS for different types of tasks and human activities, human gait remains still under-investigated. 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. 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.