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.

Recently, the development of learning-based algorithms has shown a crucial role to extract features of vital importance from multi-spectral photoacoustic imaging. In particular, advances in spectral photoacoustic unmixing algorithms can identify tissue biomarkers without a priori information. This has the potential to enhance the diagnosis and treatment of a large number of diseases. Here, we investigated the latest progress within spectral photoacoustic unmixing approaches. We evaluated the sensitivity of different unsupervised Blind Source Separation (BSS) techniques such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Non-negative Matrix Factorization (NNMF) to distinguish absorbers from spectral photoacoustic imaging. Besides, the performance of a recently developed superpixel photoacoustic unmixing (SPAX) framework has been also examined in detail. Near-infrared spectroscopy (NIRS) has been used to validate the performance of the different unmixing algorithms. Although the NNMF has shown superior unmixing performance than PCA and ICA in terms of correlation and processing time, this is still prone to unmixing misinterpretation due to spectral coloring artifact. Thus, the SPAX framework, which also compensates for the spectral coloring effect, has shown improved sensitivity and specificity of the unmixed components. In addition, the SPAX also reveals the most and less prominent tissue components from sPAI at a volumetric scale in a data-driven way. Phantom experimental measurements and in vivo studies have been conducted to benchmark the performance of the BSS algorithms and the SPAX framework.

Magnesium (Mg)-based degradable alloys have attracted substantial attention for tissue engineering applications due to their biodegradability and potential for avoiding secondary removal surgeries. However, insufficient data in the existing literature regarding Mg’s corrosion and gas formation after implantation have delayed its wide clinical application. Since the surface properties of degradable materials constantly change after contact with body fluid, monitoring the behaviour of Mg in phantoms or buffer solutions could provide some information about its physicochemical surface changes over time. Through surface analysis and spectroscopic analysis, we aimed to investigate the structural and functional properties of degradable disks. Since bubble formation may lead to inflammation and change pH, monitoring components related to acidosis near the cells is essential. To study the bubble formation in cell culture media, we used a newly developed Mg alloy (based on Mg, zinc, and calcium), pure Mg, and commercially available grade 2 Titanium (Ti) disks in Dulbecco’s Modified Eagle Medium (DMEM) solution to observe their behaviour over ten days of immersion. Using surface analysis and the information from near-infrared spectroscopy (NIRS), we concluded on the conditions associated with the medical risks of Mg alloy disintegration. NIRS is used to investigate the degradation behaviour of Mg-based disks in the cell culture media, which is correlated with the surface analysis where possible.

Near-infrared spectroscopy (NIRS) is a rapidly developing and promising technology with potential for spectrographic analysis. Understanding NIRS measurements on the implant-tissue interface for hydrogen gas formation as part of degradation is essential for interpreting the biodegradable Magnesium (Mg) based implants. This paper introduces novel NIR optical probe that can assess the state of Mg implant's degradation when in contact with biological tissues. A tissuemimicking phantom (TMP) to mimic biological tissue's optical properties helps investigate changes in reflectance spectra due to bubble formation at the implant-tissue interface. Spectra taken from different TMP samples containing biodegradable Mg and non-degradable Titanium (Ti) disk are suitable for evaluating the implant's interaction. The results show that the reflection in TMP for samples containing Mg disks, confirms the presence of hydrogen bubbles at the surface of implants. Multi-distance optical probe with depth selectivity of 3mm and 4mm has shown to be an effective tool to monitor bubble effect on different samples.

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