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.
Highlights
Human gait is one of the most important human activities that require complex coordination between different brain regions, the musculoskeletal system, and the limb
Bamdad et al (2015) review concluded that cognitive damage arising from brain injuries and neurological diseases could be reduced with the help of rehabilitation strategies involving brain–computer interface (BCI)
This study describes the impact of different feedback modalities on the performance of an EEG-based BMI that decodes motor imagery (MI) of leg flexion and extension
Summary
Human gait is one of the most important human activities that require complex coordination between different brain regions, the musculoskeletal system, and the limb. Where yi(t): output filtered signals, xm(t): the recorded sample at instant t of each electrode, T: total number of data points, M: total number of the electrode Another common spatial filter used after the CAR filter is a weighted average filter (WAVG). The results indicated that the 2-layer-GA-SVM model’s accuracy is increased by 13.8% relative to the single GA-SVM model, indicating significant improvements in detecting self-regulated intention using inter-subject BCIs. SVM performance was be studied in several EEG and fNIRS studies to enhance classification accuracy (Mihara et al, 2012; Naseer et al, 2014; Hedian et al, 2018; Kim et al, 2019). Ge et al (2017) used SVM to combine features extracted from EEG-fNIRS signals to achieve an average accuracy of 81.2% for an imaginary motor task. For other fused EEG-fNIRS applications, SVM yields an effective classification accuracy (Aghajani et al, 2017; Li et al, 2017)
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