Near-infrared Spectroscopy System Research Articles

Hybrid Functional Near-Infrared Spectroscopy System and Electromyography for Prosthetic Knee Control

The increasing number of individuals with limb loss worldwide highlights the need for advancements in prosthetic knee technology. To improve control and quality of life, integrating brain–computer communication with motor imagery offers a promising solution. This study introduces a hybrid system that combines electromyography (EMG) and functional near-infrared spectroscopy (fNIRS) to address these limitations and enhance the control of knee movements for individuals with above-knee amputations. The study involved an experiment with nine healthy male participants, consisting of two sessions: real execution and imagined execution using motor imagery. The OpenBCI Cyton board collected EMG signals corresponding to the desired movements, while fNIRS monitored brain activity in the prefrontal and motor cortices. The analysis of the simultaneous measurement of the muscular and hemodynamic responses demonstrated that combining these data sources significantly improved the classification accuracy compared to using each dataset alone. The results showed that integrating both the EMG and fNIRS data consistently achieved a higher classification accuracy. More specifically, the Support Vector Machine performed the best during the motor imagery tasks, with an average accuracy of 49.61%, while the Linear Discriminant Analysis excelled in the real execution tasks, achieving an average accuracy of 89.67%. This research validates the feasibility of using a hybrid approach with EMG and fNIRS to enable prosthetic knee control through motor imagery, representing a significant advancement potential in prosthetic technology.

Research on the Advantages and Challenges of Replacing LEDs with Lasers in Functional Near-Infrared Spectroscopy Systems Based on Advanced Signal Processing Algorithms

Abstract: Functional near infrared spectroscopy (fNIRS) has received increasing attention as a non-invasive, portable brain hemodynamic monitoring tool due to its potential for use in natural Settings. Traditional fNIRS systems commonly use light-emitting diodes (LEDs) as light sources, which have become the mainstream choice because of their low cost, easy integration, low heat output, and availability of a variety of near-infrared wavelengths. However, LEDs are limited by their low optical output power and broad wavelength range, which restrict their effectiveness in deep tissue penetration and signal quality. In contrast, Laser Diodes (LD) have the advantages of good monochromaticity and high optical output power, which can provide deeper tissue penetration and higher signal stability. Therefore, this study explores the feasibility of using lasers instead of leds in fNIRS systems and their impact on signal quality and imaging depth. By comparing the performance of LED and laser-based fNIRS systems, it is evident that lasers provide significant improvements in signal quality and imaging depth, although they face challenges in terms of cost, thermal management, and safety. The research in this paper provides an important reference for the future design of fNIRS system, especially in the application scenarios requiring high precision imaging.

Decrease in walking ability with increased functional connectivity between multiple brain areas in Parkinson's disease: a functional near-infrared spectroscopy study.

Gait disturbances significantly impact the mobility and quality of life of individuals with Parkinson's disease (PD). This study aims to delve into the cortical mechanisms underlying gait disorders in PD, specifically focusing on the prefrontal cortex (PFC), premotor cortex (PMC), and primary somatosensory cortex (PSC). To compare the functional connectivity of the PFC, PMC, and PSC regions during walking between individuals with PD and healthy controls. The study included 30 individuals with PD (mean age 62.40 ± 7.16 years) and 22 healthy older adults (mean age 60.95 ± 6.34 years). All participants were requested to walk back and forth at a comfortable pace for 30 s over a 10-meter course three times. A mobile functional near-infrared spectroscopy (fNIRS) system was employed to evaluate the oxyhemoglobin concentration change (∆HbO2). To assess the interactions between the PFC, PMC, and PSC, the connectivity strength between different fNIRS channels was computed. Individuals with PD in the off-state exhibited significantly decreased walking speed and shorter stride length compared to the healthy controls. For six brain regions including the left (L) and right (R) PFC, PMC, and PSC, no significant differences in functional connectivity within each region were found between the PD and control groups. However, when it comes to the functional connectivity between every two regions, the PD group exhibited stronger functional connectivity than the control group in the LPFC-LPMC, LPFC-RPMC, LPFC-LPSC, RPFC-LPMC, RPFC-LPSC, LPMC-LPSC, LPMC-RPSC, and RPMC-RPSC. Positive correlations were found between gait performance (speed and stride length) and functional connectivity within the RPMC as well as between the RPMC and the RPSC. Individuals with PD exhibit notable gait disturbances and increased functional connectivity in brain regions responsible for sensorimotor integration and motor function in their off-state. Strengthening the functional connectivity within the RPMC and between the RPMC and the RPSC could be a potential target for future treatments of gait impairments in PD.

Exploring the impact and influence of melanin on frequency-domain near-infrared spectroscopy measurements.

Near-infrared spectroscopy (NIRS) is a non-invasive optical method that measures changes in hemoglobin concentration and oxygenation. The measured light intensity is susceptible to reduced signal quality due to the presence of melanin. We quantify the influence of melanin concentration on NIRS measurements taken with a frequency-domain near-infrared spectroscopy system using 690 and 830nm. Using a forehead NIRS probe, we measured 35 healthy participants and investigated the correlation between melanin concentration indices, which were determined using a colorimeter, and several key metrics from the NIRS signal. These metrics include signal-to-noise ratio (SNR), two measurements of oxygen saturation (arterial oxygen saturation, , and tissue oxygen saturation, ), and optical properties represented by the absorption coefficient ( ) and the reduced scattering coefficient ( ). We found a significant negative correlation between the melanin index and the SNR estimated in oxy-hemoglobin signals ( , ) and levels ( , ). However, no significant changes were observed in the optical properties and ( , ). We found that estimated SNR and values show a significant decline and dependence on the melanin index, whereas and optical properties do not show any correlation with the melanin index.

Bootstrap-integrated machine learning techniques for the calibration of near-infrared (NIR) spectra

Near-infrared (NIR) spectroscopy system is frequently used in food production because of its superior characteristics. Establishing models on spectral data for prediction has always been of interest. However, no satisfactory model has been developed on different data types for accuracy and reliability. In this study, an approach strategy is reported for the calibration of NIR spectra by utilizing a combination of the bootstrap technique and an ensemble method. The approach comprises three steps. First, data are resampled to create bootstrap samples. Second, four calibration models are applied to the grouped data: partial least squares regression (PLSR), support vector regression (SVR), backpropagation neural network (BPNN), and principal component analysis-backpropagation neural network (PCA-BPNN). Finally, predictions obtained after the calibration are combined using the ensemble method. The data studied include 215, 32, and 540 samples, which were characterized as hyperspectral, small sample, and categorical data, respectively. Root mean square error of prediction (RMSEP), R squared ( R 2 ), and residual prediction deviation (RPD) were used to describe the accuracy. Coverage probability of the prediction interval (PICP) and normalized average prediction interval width (NMPIW) were utilized to evaluate the reliability. The accuracies of the methods exhibited the following order: the proposed method > PLSR > SVR > BPNN ≈ PCA-BPNN. The results confirm that the reported model achieved satisfactory accuracy and was more reliable than the single calibration models.

Investigating the effect of limited spectral information on NIRS-derived changes in hemoglobin and cytochrome-c-oxidase concentration with a diffusion-based model.

This paper investigates the theoretical capability of near-infrared spectroscopy (NIRS) systems to accurately measure changes in the oxidation state of cerebral cytochrome-c-oxidase (CCO) alongside the hemoglobins, for a deeper understanding of NIRS limitations. Concentration changes of oxy and deoxyhemoglobin (HbO and HbR) indicate the oxygen status of blood vessels and correlate with several other physiological parameters across different pathologies. The oxidation state of CCO indicates cellular energy usage efficiency through oxidative metabolism, potentially serving as a biomarker for brain and other tissue disorders. This study employs an analytical model based on the diffusion equation and statistical analyses to explore the dependency of estimated concentration changes on various systematic parameters, such as choice of wavelengths, spectral bandwidth, and uncertainties in extinction coefficient (ε) and differential pathlength factor (DPF). When there is a 10% uncertainty in DPF and ε, errors were found to be highly dependent on the number of discrete wavelengths, but not on their bandwidth if appropriate considerations are taken to account for it.

Prefrontal cortex activation under stress as a function of borderline personality disorder in female adolescents engaging in non-suicidal self-injury.

Neuroimaging studies suggest alterations in prefrontal cortex (PFC) activity in healthy adults under stress. Adolescents with non-suicidal self-injury (NSSI) report difficulties in stress and emotion regulation, which may be dependent on their level of borderline personality disorder (BPD). The aim was to examine alterations in the PFC in adolescents with NSSI during stress. Adolescents (13-17 years) engaging in non-suicidal self-injury (n = 30) and matched healthy controls (n = 29) performed a task with low cognitive demand and the Trier Social Stress Test (TSST). Mean PFC oxygenation across the PFC was measured with an eight-channel near-infrared spectroscopy system. Alongside self-reports on affect, dissociation and stress, BPD pathology was assessed via clinical interviews. Mixed linear-effect models revealed a significant effect of time on PFC oxygenation and a significant time×group interaction, indicating increased PFC activity in patients engaging in NSSI at the beginning of the TSST compared with healthy controls. Greater BPD symptoms overall were associated with an increase in PFC oxygenation during stress. In exploratory analyses, mixed models addressing changes in PFC connectivity over time as a function of BPD symptoms were significant only for the left PFC. Results indicate differences in the neural stress response in adolescents with NSSI in line with classic neuroimaging findings in adults with BPD. The link between PFC oxygenation and measures of BPD symptoms emphasises the need to further investigate adolescent risk-taking and self-harm across the spectrum of BPD, and maybe overall personality pathology, and could aid in the development of tailored therapeutic interventions.

Determination of soluble solids content in loquat using near-infrared spectroscopy coupled with broad learning system and hybrid wavelength selection strategy

Soluble solids content (SSC) serves as a crucial metric in assessing loquat quality. To achieve fast, non-destructive, and accurate detection of SSC, the combination of near-infrared (NIR) spectroscopy and broad learning system (BLS) model was developed in this study. Meanwhile, the influence of six spectral preprocessing methods and six wavelength selection algorithms on SSC prediction was explored, and the performance of the BLS model was compared with the widely used partial least squares regression (PLSR) and least squares support vector machine (LS-SVM) models. The results showed that Savitzky-Golay smoothing combined with standard normal transformation (SG-SNV) offered the optimal pretreatment, and the hybrid method, namely interval variable iterative space shrinking analysis and successive projections algorithm (iVISSA-SPA), emerged as the most effective wavelength selection method. The BLS model outperformed both PLSR and LS-SVM models. Specifically, the BLS model based on the iVISSA-SPA method achieved the optimal SSC prediction results, with RP2 = 0.8646, RMSEP = 0.5104, and RPD = 2.7481. Therefore, NIR spectroscopy coupled with the BLS model and hybrid wavelength selection strategy could rapidly, non-destructively and accurately detect the SSC of loquat, providing a viable alternative for SSC prediction in fruit.

Reliability of brain metrics derived from a Time-Domain Functional Near-Infrared Spectroscopy System

With the growing interest in establishing brain-based biomarkers for precision medicine, there is a need for noninvasive, scalable neuroimaging devices that yield valid and reliable metrics. Kernel’s second-generation Flow2 Time-Domain Functional Near-Infrared Spectroscopy (TD-fNIRS) system meets the requirements of noninvasive and scalable neuroimaging, and uses a validated modality to measure brain function. In this work, we investigate the test-retest reliability (TRR) of a set of metrics derived from the Flow2 recordings. We adopted a repeated-measures design with 49 healthy participants, and quantified TRR over multiple time points and different headsets—in different experimental conditions including a resting state, a sensory, and a cognitive task. Results demonstrated high reliability in resting state features including hemoglobin concentrations, head tissue light attenuation, amplitude of low frequency fluctuations, and functional connectivity. Additionally, passive auditory and Go/No-Go inhibitory control tasks each exhibited similar activation patterns across days. Notably, areas with the highest reliability were in auditory regions during the auditory task, and right prefrontal regions during the Go/No-Go task, consistent with prior literature. This study underscores the reliability of Flow2-derived metrics, supporting its potential to actualize the vision of using brain-based biomarkers for diagnosis, treatment selection and treatment monitoring of neuropsychiatric and neurocognitive disorders.

The Time Course of Changes in Prefrontal Cortex Activity During Walking in People With Parkinson’s Disease

Background Walking abnormalities in people with Parkinson’s disease (PD) are characterized by a shift in locomotor control from healthy automaticity to compensatory, executive control, mainly located in the prefrontal cortex (PFC). Although PFC activity during walking increases in people with PD, the time course of PFC activity during walking and its relationship to clinical or gait characteristics is unknown. Objective To identify the time course of PFC activity during walking in people with PD. To investigate whether clinical or gait variables would explain the PFC activity changes. Methods Thirty-eight people with PD tested OFF medication wore a portable, functional near-infrared spectroscopy (fNIRS) system to record relative PFC activity while walking. Wearable inertial sensors recorded spatiotemporal gait characteristics. Based on the PFC activity (fNIRS) in the late phase of the walking task (final 40 seconds), compared to the early phase (initial 40 seconds), participants were separated into 2 groups: reduced or sustained PFC activity. Results People with PD who reduced PFC activity during walking had less impaired gait (eg, faster gait speed) than those who had a sustained increase in PFC activity (P < .05). Cognitive set-shifting ability explained 18% of the PFC activation in the group with a sustained increase in PFC activity (P = .033). Conclusions The time course of reduction in PFC activity corresponds to less impaired gait performance in people with PD, while a sustained increase in PFC activity is related to worse cognitive flexibility. Reduction in PFC activity while walking may indicate a less impaired, automatic control of walking.

Investigating cortical hypoxia in multiple sclerosis via time-domain near-infrared spectroscopy.

Hypoperfusion and tissue hypoxia have been implicated as contributory mechanisms in the neuropathology of multiple sclerosis (MS). Our objective has been to study cortical oxygenation in vivo in patients with MS and age-matched controls. A custom, multiwavelength time-domain near-infrared spectroscopy system was developed for assessing tissue hypoxia from the prefrontal cortex. A cross-sectional case-control study was undertaken assessing patients with secondary progressive MS (SPMS) and age-matched controls. Co-registered magnetic resonance imaging was used to verify the location from which near-infrared spectroscopy data were obtained through Monte Carlo simulations of photon propagation. Additional clinical assessments of MS disease severity were carried out by trained neurologists. Linear mixed effect models were used to compare cortical oxygenation between cases and controls, and against measures of MS severity. Thirty-three patients with secondary progressive MS (median expanded disability status scale 6 [IQR: 5-6.5]; median age 53.0 [IQR: 49-58]) and 20 age-matched controls were recruited. Modeling of photon propagation confirmed spectroscopy data were obtained from the prefrontal cortex. Patients with SPMS had significantly lower cortical hemoglobin oxygenation compared with controls (-6.0% [95% CI: -10.0 to -1.9], P = 0.004). There were no significant associations between cortical oxygenation and MS severity. Using an advanced, multiwavelength time-domain near-infrared spectroscopy system, we demonstrate that patients with SPMS have lower cortical oxygenation compared with controls.

Dual-Task Effect on Center of Pressure Oscillations and Prefrontal Cortex Activation Between Young and Older Adults

ABSTRACT Purpose: This study aimed to investigate the dual-task effect on conventional center of pressure (CoP) outcomes, CoP oscillations, and prefrontal cortex (PFC) activation between young and older adults. Methods: Fourteen healthy older adults (age: 66.25 ± 3.43 years) and another fourteen gender-matched young adults (age: 19.80 ± 0.75 years) participated in this study. Participants completed single-task and dual-task standing trials in a fixed order. The displacement of CoP and PFC activation were recorded using a Force plate and a functional near-infrared spectroscopy system, respectively. Two-way MANOVAs were used to examine the group and task effects. Additionally, the Pearson correlation analyses were used to investigate the relationship between CoP oscillations and PFC activation. Results: Our results showed a worse balance performance, greater CoP oscillations of 0–0.1 (11.03 ± 8.24 vs. 23.20 ± 12.54 cm2) and 0.1–0.5 (13.62 ± 9.30 vs. 30.00 ± 23.12 cm2) Hz in the medial-lateral direction and higher right (dorsomedial: −0.0003 ± 0.021 vs. 0.021 ± 0.021 & ventrolateral: 0.0087 ± 0.047 vs. 0.025 ± 0.045 mol/ml) and left (dorsomedial: 0.0033 ± 0.024 vs. 0.020 ± 0.025 & ventrolateral: 0.0060 ± 0.037 vs. 0.034 ± 0.037 mol/ml) PFC activation in response to a secondary cognitive task in older adults (p < .05). Older adults also showed significant positive correlations between CoP oscillations in the anterior-posterior direction and PFC activation under the single-task standing. Conclusion: These results suggest that older adults presented a loss of postural automaticity contributing to cognitive dysfunction. Moreover, heightened CoP oscillations at 0–0.5 Hz in response to a secondary cognitive task could provide evidence of a loss of automaticity, which might be associated with a greater reliance on the sensory inputs.

Exploratory insights into prefrontal cortex activity in continuous glucose monitoring: findings from a portable wearable functional near-infrared spectroscopy system.

The escalating global prevalence of diabetes highlights an urgent need for advancements in continuous glucose monitoring (CGM) technologies that are non-invasive, accurate, and user-friendly. Here, we introduce a groundbreaking portable wearable functional near-infrared spectroscopy (fNIRS) system designed to monitor glucose levels by assessing prefrontal cortex (PFC) activity. Our study delineates the development and application of this novel fNIRS system, emphasizing its potential to revolutionize diabetes management by providing a non-invasive, real-time monitoring solution. Fifteen healthy university students participated in a controlled study, where we monitored their PFC activity and blood glucose levels under fasting and glucose-loaded conditions. Our findings reveal a significant correlation between PFC activity, as measured by our fNIRS system, and blood glucose levels, suggesting the feasibility of fNIRS technology for CGM. The portable nature of our system overcomes the mobility limitations of traditional setups, enabling continuous, real-time monitoring in everyday settings. We identified 10 critical features related to blood glucose levels from extensive fNIRS data and successfully correlated PFC function with blood glucose levels by constructing predictive models. Results show a positive association between fNIRS data and blood glucose levels, with the PFC exhibiting a clear response to blood glucose. Furthermore, the improved regressive rule principal component analysis (PCA) method outperforms traditional PCA in model prediction. We propose a model validation approach based on leave-one-out cross-validation, demonstrating the unique advantages of K-nearest neighbor (KNN) models. Comparative analysis with existing CGM methods reveals that our paper's KNN model exhibits lower RMSE and MARD at 0.11 and 8.96%, respectively, and the fNIRS data were highly significant positive correlation with actual blood glucose levels (r = 0.995, p < 0.000). This study provides valuable insights into the relationship between metabolic states and brain activity, laying the foundation for innovative CGM solutions. Our portable wearable fNIRS system represents a significant advancement in effective diabetes management, offering a promising alternative to current technologies and paving the way for future advancements in health monitoring and personalized medicine.

Quantifying changes in oxygen saturation of the internal jugular vein in vivo using deep neural networks and subject-specific three-dimensional Monte Carlo models.

Central venous oxygen saturation (ScvO2) is an important parameter for assessing global oxygen usage and guiding clinical interventions. However, measuring ScvO2 requires invasive catheterization. As an alternative, we aim to noninvasively and continuously measure changes in oxygen saturation of the internal jugular vein (SijvO2) by a multi-channel near-infrared spectroscopy system. The relation between the measured reflectance and changes in SijvO2 is modeled by Monte Carlo simulations and used to build a prediction model using deep neural networks (DNNs). The prediction model is tested with simulated data to show robustness to individual variations in tissue optical properties. The proposed technique is promising to provide a noninvasive tool for monitoring the stability of brain oxygenation in broad patient populations.

Patients with chronic ankle instability exhibit increased sensorimotor cortex activation and correlation with poorer lateral balance control ability during single-leg stance: a FNIRS study.

Chronic Ankle Instability (CAI) is a musculoskeletal condition that evolves from acute ankle sprains, and its underlying mechanisms have yet to reach a consensus. Mounting evidence suggests that neuroplastic changes in the brain following ankle injuries play a pivotal role in the development of CAI. Balance deficits are a significant risk factor associated with CAI, yet there is a scarcity of evidence regarding the sensorimotor cortical plasticity related to balance control in affected individuals. This study aims to evaluate the differences in cortical activity and balance abilities between patients with CAI and uninjured individuals during a single-leg stance, as well as the correlation between these factors, in order to elucidate the neurophysiological alterations in balance control among patients with CAI. The study enrolled 24 patients with CAI and 24 uninjured participants. During single-leg stance, cortical activity was measured using a functional near-infrared spectroscopy (fNIRS) system, which included assessments of the pre-motor cortex (PMC), supplementary motor area (SMA), primary motor cortex (M1), and primary somatosensory cortex (S1). Concurrently, balance parameters were tested utilizing a three-dimensional force platform. Independent sample t-tests revealed that, compared with the uninjured individuals, the patients with CAI exhibited a significant increase in the changes of oxyhemoglobin concentration (ΔHbO) during single-leg stance within the left S1 at Channel 5 (t = 2.101, p = 0.041, Cohen's d = 0.607), left M1 at Channel 6 (t = 2.363, p = 0.022, Cohen's d = 0.682), right M1 at Channel 15 (t = 2.273, p = 0.029, Cohen's d = 0.656), and right PMC/SMA at Channel 11 (t = 2.467, p = 0.018, Cohen's d = 0.712). Additionally, the center of pressure root mean square (COP-RMS) in the mediolateral (ML) direction was significantly greater (t = 2.630, p = 0.012, Cohen's d = 0.759) in the patients with CAI. Furthermore, a moderate positive correlation was found between ML direction COP-RMS and ΔHbO2 in the M1 (r = 0.436; p = 0.033) and PMC/SMA (r = 0.488, p = 0.016), as well as between anteroposterior (AP) direction COP-RMS and ΔHbO in the M1 (r = 0.483, p = 0.017). Patients with CAI demonstrate increased cortical activation in the bilateral M1, ipsilateral PMC/SMA, and contralateral S1. This suggests that patients with CAI may require additional brain resources to maintain balance during single-leg stance, representing a compensatory mechanism to uphold task performance amidst diminished lateral balance ability in the ankle joint.

An Ultralow-Power Real-Time Machine Learning Based fNIRS Motion Artifacts Detection.

Due to iterative matrix multiplications or gradient computations, machine learning modules often require a large amount of processing power and memory. As a result, they are often not feasible for use in wearable devices, which have limited processing power and memory. In this study, we propose an ultralow-power and real-time machine learning-based motion artifact detection module for functional near-infrared spectroscopy (fNIRS) systems. We achieved a high classification accuracy of 97.42%, low field-programmable gate array (FPGA) resource utilization of 38354 lookup tables and 6024 flip-flops, as well as low power consumption of 0.021 W in dynamic power. These results outperform conventional CPU support vector machine (SVM) methods and other state-of-the-art SVM implementations. This study has demonstrated that an FPGA-based fNIRS motion artifact classifier can be exploited while meeting low power and resource constraints, which are crucial in embedded hardware systems while keeping high classification accuracy.

Combined frequency domain near-infrared spectroscopy and diffuse correlation spectroscopy system for comprehensive metabolic monitoring of inspiratory muscles during loading.

Mechanical ventilation (MV) is a cornerstone technology in the intensive care unit as it assists with the delivery of oxygen in critically ill patients. The process of weaning patients from MV can be long and arduous and can lead to serious complications for many patients. Despite the known importance of inspiratory muscle function in the success of weaning, current clinical standards do not include direct monitoring of these muscles. The goal of this project was to develop and validate a combined frequency domain near-infrared spectroscopy (FD-NIRS) and diffuse correlation spectroscopy (DCS) system for the noninvasive characterization of inspiratory muscle response to a load. The system was fabricated by combining a custom digital FD-NIRS and DCS system. It was validated via liquid phantom titrations and a healthy volunteer study. The sternocleidomastoid (SCM), an accessory muscle of inspiration, was monitored during a short loading period in fourteen young, healthy volunteers. Volunteers performed two different respiratory exercises, a moderate load and a high load, which consisted of a one-minute baseline, a one-minute load, and a six-minute recovery period. The system has low crosstalk between absorption, reduced scattering, and flow when tested in a set of liquid titrations. Faster dynamics were observed for changes in blood flow index (), and metabolic rate of oxygen () compared with hemoglobin + myoglobin (Hb+Mb) based parameters after the onset of loads in males. Additionally, larger percent changes in , and were observed compared with Hb+Mb parameters in both males and females. There were also sex differences in baseline values of oxygenated Hb+Mb, total Hb+Mb, and tissue saturation. The dynamic characteristics of Hb+Mb concentration and blood flow were distinct during loading of the SCM, suggesting that the combination of FD-NIRS and DCS may provide a more complete picture of inspiratory muscle dynamics.

Reliability Evaluation for Continuous-Wave Functional Near-Infrared Spectroscopy Systems: Comprehensive Testing from Bench Characterization to Human Test.

In recent years, biomedical optics technology has developed rapidly. The current widespread use of biomedical optics was made possible by the invention of optical instruments. The advantages of being non-invasive, portable, effective, low cost, and less susceptible to system noise have led to the rapid development of functional near-infrared spectroscopy (fNIRS) technology for hemodynamics detection, especially in the field of functional brain imaging. At the same time, laboratories and companies have developed various fNIRS-based systems. The safety, stability, and efficacy of fNIRS systems are key performance indicators. However, there is still a lack of comprehensive and systematic evaluation methods for fNIRS instruments. This study uses the fNIRS system developed in our laboratory as the test object. The test method established in this study includes system validation and performance testing to comprehensively assess fNIRS systems' reliability. These methods feature low cost and high practicality. Based on this study, existing or newly developed systems can be comprehensively and easily evaluated in the laboratory or workspace.

Prefrontal engagement predicts the effect of museum visit on psychological well-being: an fNIRS exploration.

Recent research suggests that museum visits can benefit psychological well-being by reducing symptoms of stress and anxiety. However, these reported relaxing effects remain inconsistent between studies. Shedding light on the underlying cerebral mechanisms of museum visits might support a better understanding of how it affects psychological well-being. This study aimed to investigate the prefrontal engagement evoked by artwork analysis during a museum visit and to determine if these prefrontal substrates are associated with the museum's effect on psychological well-being in older adults. Nineteen adults aged between 65 and 79, toured a Baroque-style exhibit at the Montreal Museum of Fine Arts for approximately 20 minutes while equipped with a near-infrared spectroscopy system measuring the prefrontal cortex's hemodynamic activity. For each painting, participants received the instruction to either (1): analyze the painting and produce a personal interpretation of its signification (analytic condition) or (2) visualize the painting without any specific thoughts (visualization condition). Questionnaires measuring stress, anxiety, and well-being were administered before and after the visit. Sixteen older women (71.5 ± 4 years) were included in the analyses. Results showed that, at the group level, the analytic condition was associated with an increased activation pattern in the left ventrolateral prefrontal region, typically related to attentional processes (not observed in the visualization condition). The activation associated with the analytic condition predicted pre-/post-visit reductions in self-reported anxiety and stress in the sample of older women. These observations suggest that the level of engagement of attentional processes during artwork analysis may play a major role in the effect of a museum's visit on self-reported symptoms of anxiety.

Cerebral oxygenation and perfusion kinetics monitoring of military aircrew at high G using novel fNIRS wearable system.

Real-time physiological episode (PE) detection and management in aircrew operating high-performance aircraft (HPA) is crucial for the US Military. This paper addresses the unique challenges posed by high acceleration (G-force) in HPA aircrew and explores the potential of a novel wearable functional near-infrared spectroscopy (fNIRS) system, named NIRSense Aerie, to continuously monitor cerebral oxygenation during high G-force exposure. The NIRSense Aerie system is a flight-optimized, wearable fNIRS device designed to monitor tissue oxygenation 13-20 mm below the skin's surface. The system includes an optical frontend adhered to the forehead, an electronics module behind the earcup of aircrew helmets, and a custom adhesive for secure attachment. The fNIRS optical layout incorporates near-distance, middle-distance, and far-distance infrared emitters, a photodetector, and an accelerometer for motion measurements. Data processing involves the modified Beer-Lambert law for computing relative chromophore concentration changes. A human evaluation of the NIRSense Aerie was conducted on six subjects exposed to G-forces up to +9 Gz in an Aerospace Environmental Protection Laboratory centrifuge. fNIRS data, pulse oximetry, and electrocardiography (HR) were collected to analyze cerebral and superficial tissue oxygenation kinetics during G-loading and recovery. The NIRSense Aerie successfully captured cerebral deoxygenation responses during high G-force exposure, demonstrating its potential for continuous monitoring in challenging operational environments. Pulse oximetry was compromised during G-loading, emphasizing the system's advantage in uninterrupted cerebrovascular monitoring. Significant changes in oxygenation metrics were observed across G-loading levels, with distinct responses in Deoxy-Hb and Oxy-Hb concentrations. HR increased during G-loading, reflecting physiological stress and the anti-G straining maneuver. The NIRSense Aerie shows promise for real-time monitoring of aircrew physiological responses during high G-force exposure. Despite challenges, the system provides valuable insights into cerebral oxygenation kinetics. Future developments aim for miniaturization and optimization for enhanced aircrew comfort and wearability. This technology has potential for improving anti-G straining maneuver learning and retention through real-time cerebral oxygenation feedback during centrifuge training.