Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

Abstract

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.

A Survey of Human Gait-Based Artificial Intelligence Applications

“…Fusing different bio-signals in hBCI systems for gait applications improves classification accuracy and the number of control commands. Still, it introduces the problem of channel configuration, information transfer rate, and temporal synchronization between the modalities ( Khan et al, 2021 ).…”

Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks

“…Well-designed wearable assistive devices for rehabilitation and performance augmentation purposes have been developed that run independently of physical or muscular interventions [22][23][24]. Recent studies focus on acquiring the user's intent through brain signals to control these devices/limbs [25][26][27].…”

Review on Patient-Cooperative Control Strategies for Upper-Limb Rehabilitation Exoskeletons

“…Moreover, it should adapt to patients’ status and recovery stage, both throughout the single movement and over the rehabilitation treatment ( Marchal-Crespo and Reinkensmeyer, 2009 ). In addition, there is a great effort in the scientific community to develop frameworks that take advantage of non-invasive and portable brain monitor techniques (e.g., EEG Noda et al (2012) ; Nicolas-Alonso and Gomez-Gil (2012) , fNIRS Hong et al (2020) ; Khan et al (2021) ). Such approaches are employed to detect user intention (i.e., brain-machine interface) and to directly evaluate motor recovery in terms of neural plasticity, making the framework even more complex.…”