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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.

Limited non-invasive transhumeral prosthesis control exists due to the absence of signal sources on amputee residual muscles. This paper introduces a hybrid brain-machine interface (hBMI) that integrates surface electromyography (sEMG) and functional near-infrared spectroscopy (fNIRS) signals to overcome the limits of existing myoelectric upper-limb prosthesis. This hybridization aims to improve classification accuracy (CA) to escalate arm movements' control performance for individuals who have transhumeral amputation. To evaluate the effectiveness of this hBMI, fifteen healthy and three transhumeral amputee subjects for six arm motions were participating in the experiment. Myo armband was used to acquire sEMG signals corresponding to four arm motions: elbow extension, elbow flexion, wrist pronation, and wrist supination. Whereas fNIRS brain imaging modality was used to monitor cortical hemodynamics response from the prefrontal cortex region for two hand motions: hand open and hand close. The average accuracy of 94.6 % and 74% was achieved for elbow and wrist motions by sEMG for healthy and amputated subjects, respectively. Simultaneously, the fNIRS modality showed an average accuracy of 96.9% and 94.5% for hand motions of healthy and amputated subjects. This study demonstrates the feasibility of hybridizing sEMG and fNIRS signals to improve the CA for transhumeral amputees, improving the control performances of multifunctional upper-limb prostheses.INDEX TERMS Classification accuracy, fNIRS, hybrid brain-machine interface, sEMG, transhumeral prosthesis.

This work demonstrates a simple approach for coating a porous polymer layer on stainless-steel (SS) microneedles characterized by a pH-responsive formulation for self-regulated drug delivery. For many drug-delivery applications, the release of therapeutic agents in an acidic microenvironment is desirable. Acid-sensitive polymers and hydrogels were extensively explored, but easily prepared polymeric microcarriers that combine acid sensitivity and biodegradability are rare. Here, we describe a simple and robust method of coating a porous polymer layer on SS microneedles (MNs) that release a model drug (lidocaine) in a pH-responsive fashion. It was constructed by packing the model drug and a pH-sensitive component (sodium bicarbonate) into the pores of the polymer layer. When this acid-sensitive formulation was exposed to the acidic microenvironment, the consequent reaction of protons (H+) with sodium bicarbonate (NaHCO3) yielded CO2. This effect generated pressure inside the pores of the coating and ruptured the thin polymer membrane, thereby releasing the encapsulated drug. Scanning electron micrographs showed that the pH-sensitive porous polymer-coated MNs exposed to phosphate-buffered saline (PBS) at pH 7.4 were characterized by closed pores. However, MNs exposed to PBS at pH 5.5 consisted of open pores and the thin membrane burst. The in vitro studies demonstrated the pH sensitivity of the drug release from porous polymer-coated MNs. Negligible release was observed for MNs in receiving media at pH 7.4. In contrast, significant release occurred when the MNs were exposed to acidic conditions (pH 5.5). Additionally, comparable results were obtained for drug release in vitro in porcine skin and in PBS. This revealed that our developed pH-responsive porous polymer-coated MNs could potentially be used for the controlled release of drug formulations in an acidic environment. Moreover, the stimuli-responsive drug carriers will enable on-demand controlled release profiles that may enhance therapeutic effectiveness and reduce systemic toxicity.

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