SignificanceRepetitive transcranial magnetic stimulation (rTMS) has emerged as a promising noninvasive neuromodulation technique for patients with disorders of consciousness (DOC). However, the specific effects of rTMS stimulation parameters on this population remain insufficiently characterized.ObjectivesFunctional near-infrared spectroscopy (fNIRS) was used to evaluate the real-time and short-term effects of rTMS with different stimulation parameters on patients with DOC.MethodsThe hemodynamic data were acquired from 20 patients with DOC during rTMS at different frequencies (10 Hz and 20 Hz) and different target sites (at F3 and F4), yielding four experimental conditions. The spatiotemporal characteristics of hemodynamics across these conditions were quantitatively compared to investigate the real-time and short-term after-effects of rTMS.ResultsResults showed that the hemodynamic responses significantly increased following the high- frequency (both 10 Hz and 20 Hz) rTMS. With a fixed total number of stimulation pulses, the lower-frequency, longer-duration protocol (10 Hz, 10 s) produced stronger hemodynamic responses than the higher-frequency, shorter-duration protocol (20 Hz, 5 s) across measured brain areas. Notably, rTMS applied to either the left (F3) or right (F4) DLPFC evoked significantly stronger ipsilateral than contralateral hemodynamic responses, indicating a lateralized neuromodulation effect.ConclusionThe fNIRS-based monitoring offers a valuable method for evaluating the neuromodulation efficacy of rTMS, providing a novel perspective for optimizing neuromodulation parameters for patients with DOC.

Introdution: Spinal cord stimulation (SCS) has emerged as a promising neuromodulatory intervention for patients with disorders of consciousness (DoC). However, the identification of optimal stimulation frequencies remains a subject of ongoing debate. Although previous electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) studies have suggested the therapeutic efficacy of 5- and 70-Hz, respectively, the integrative neurovascular mechanisms and frequency-specific network dynamics underlying these effects remain to be elucidated. Objective and Impact Statement: This study aims to characterize frequency-dependent network reconfiguration in DoC using simultaneous EEG-fNIRS recordings and graph theoretical analysis. By delineating distinct neurophysiological and hemodynamic signatures, our findings establish a mechanistic framework for the optimization of SCS parameters, thereby advancing personalized neuromodulation strategies for the promotion of consciousness recovery. Methods: This prospective trial used simultaneous EEG-fNIRS and graph theory in 16 patients with DoC undergoing multifrequency SCS at 5, 20, 70, and 100 Hz to decode frequency-specific network dynamics. Our integrated EEG-fNIRS analysis revealed 3 principal advances. First, multimodal cortical mapping via a unified anatomical atlas quantified frequency-dependent network reconfiguration, generating graph-theoretical metrics (global and nodal efficiency, characteristic path length, and clustering coefficients) from source-localized EEG (delta-gamma bands) and fNIRS (oxyhemoglobin and deoxygenated) data. Second, we identified frequency-dependent neurophysiological profiles. Results: Five-hertz stimulation produced acute enhancement of theta-band global network efficiency coupled with elevated gamma-band nodal efficiency in the right cingulate motor area, indicating immediate frontolimbic engagement. Conversely, 70-Hz stimulation selectively evoked delayed hemodynamic responses in the visual cortices and increased occipital hemoglobin oxygenation without concomitant EEG alterations, suggesting preferential retinotopic pathway recruitment. Conclusion: Multimodal EEG-fNIRS analysis elucidates frequency-specific SCS mechanisms, where 5-Hz stimulation optimizes local information integration through theta and gamma modulation, while 70-Hz enhances long-range connectivity, exposing frequency-specific neural plasticity mechanisms.

Results of digital speckle pattern interferometry (DSPI) measurements are subject to errors due to the superposition of multiple source errors. Currently, the promotion and application of this technique are hindered due to the lack of effective calibration methods. In this article, a DSPI three-axis angular motion measurement error model is established, and a comprehensive calibration and accuracy evaluation method based on the three-axis angular motion measurement theory is proposed to achieve accurate calibration and accuracy evaluation of DSPI systems. Experiments showed that the average relative errors of pitch, yaw, and roll angle measurements are reduced from 5.36%, 5.04% and 4.8% to 4.05%, 2.62% and 2.04%, respectively, after the calibration. The proposed method can effectively improve the DSPI measurement accuracy and also evaluate the accuracy of the DSPI measurement system.

OBJECTIVE:The current application of single-modality electroencephalography (EEG) or functional near-infrared spectroscopy (fNIRS) to assess consciousness levels in patients with disorders of consciousness (DoC) has garnered significant attention. However, the diagnostic accuracy of unimodal approaches remains suboptimal. Therefore, this study aims to apply the multimodal fusion technology of EEG and fNIRS to the clinical diagnosis of DoC patients. METHODS:Eleven patients with DoC (six with a minimally conscious state [MCS] and five with a vegetative state [VS]) were enrolled. The motor intention-based brain-computer interface (MI-BCI) paradigm was adopted. EEG and fNIRS were recorded simultaneously. The synchronous states of EEG and fNIRS were analyzed, including time-frequency analysis, event-related desynchronization (ERD), and changes in oxy-hemoglobin (HbO)/de-oxygenated (HbR)/total hemoglobin (HbT) content. A multimodal method combining EEG and fNIRS was used to classify DoC patients. RESULTS:The machine-learning results of the MI-BCI model showed that the EEG-fNIRS multimodal approach was superior to single-modality techniques in the diagnosis of healthy controls (HC), MCS, and VS. The multimodal model achieved a mean AUC of 0.69 ± 0.10, significantly outperforming both unimodal EEG (0.43 ± 0.19; p < 0.01) and standalone fNIRS (0.63 ± 0.10; p < 0.05). The EEG_ERD index of left-handed MI-BCI significantly differentiated the MCS and VS groups. Meanwhile, for the classification tasks of HC, MCS, and VS, the importance ranking of the indicators was as follows: fNIRS_ACC, EEG_ACC, fNIRS_slope, fNIRS_centroid, EEG_ERD, fNIRS_integral, and fNIRS_mean. CONCLUSION:The integration of multimodal MI-BCI paradigms demonstrates clinical potential in evaluating consciousness levels, while the synergistic combination of neurophysiological and hemodynamic biomarkers provides a robust framework for enhancing the precision of bedside diagnostic protocols. TRIAL REGISTRATION:Clinical Trial Registry: ChiCTR2400085830.

Significance:Disorders of consciousness (DOCs) pose significant challenges for therapeutic intervention. Spinal cord stimulation (SCS) has emerged as a promising neuromodulation technique for treating DOC patients. However, the selection of optimal SCS stimulation parameters, particularly intensity, lacks objective standards, and considerable variations in the configuration of stimulation intensity are evident among different research groups in previous studies. Aim:We aim to systematically evaluate the effects of different stimulation intensities of SCS using functional near-infrared spectroscopy (fNIRS) to further optimize the efficacy of SCS. Approach:Eleven DOC patients with implanted SCS devices were recruited. Four different stimulation intensities based on individual motor thresholds were used: low (50%), threshold (100%), medium (125%), and high (150%). Hemodynamic responses were recorded using fNIRS, and the mean, peak, and net area under the curve values of hemodynamics, as well as the activated channel count, were analyzed, mainly focusing on two regions of interest: the prefrontal cortex (PFC) and the temporo-parietal junction (TPJ). Results:An inverted U-shaped dose-response curve was observed. The medium-intensity group triggered the most significant hemodynamic responses. The high-intensity group evoked less pronounced responses and showed negative responses post-stimulation. The threshold-intensity group exhibited positive responses but less pronounced than the medium- and high-intensity groups. Conversely, the low-intensity SCS evoked a decreased response. The medium-intensity SCS also resulted in the highest number of activated channels and maintained the highest total hemoglobin concentration level during the inter-stimulus interval. Differences in brain region responses to SCS intensity were observed, with the PFC tolerating higher intensities and the TPJ having a narrower therapeutic window. Conclusions:Our findings illustrate that the medium-intensity SCS provides the optimal hemodynamic effect. The observed inverted U-shaped dose-response curve underscores the importance of precise parameter adjustments in SCS for DOC patients to maximize efficacy and to avoid overstimulation or insufficient activation.

BackgroundAdvances in neuroimaging have significantly enhanced our understanding of brain function, providing critical insights into the diagnosis and management of disorders of consciousness (DoC). Functional near-infrared spectroscopy (fNIRS), with its real-time, portable, and noninvasive imaging capabilities, has emerged as a promising tool for evaluating functional brain activity and nonrecovery potential in DoC patients. This review explores the current applications of fNIRS in DoC research, identifies its limitations, and proposes future directions to optimize its clinical utility.AimThis review examines the clinical application of fNIRS in monitoring DoC. Specifically, it investigates the potential value of combining fNIRS with brain-computer interfaces (BCIs) and closed-loop neuromodulation systems for patients with DoC, aiming to elucidate mechanisms that promote neurological recovery.MethodsA systematic analysis was conducted on 155 studies published between January 1993 and October 2024, retrieved from the Web of Science Core Collection database.ResultsAnalysis of 21 eligible studies on neurological diseases involving 262 DoC patients revealed significant findings. The prefrontal cortex was the most frequently targeted brain region. fNIRS has proven crucial in assessing brain functional connectivity and activation, facilitating the diagnosis of DoC. Furthermore, fNIRS plays a pivotal role in diagnosis and treatment through its application in neuromodulation techniques such as deep brain stimulation (DBS) and spinal cord stimulation (SCS).ConclusionAs a noninvasive, portable, and real-time neuroimaging tool, fNIRS holds significant promise for advancing the assessment and treatment of DoC. Despite limitations such as low spatial resolution and the need for standardized protocols, fNIRS has demonstrated its utility in evaluating residual brain activity, detecting covert consciousness, and monitoring therapeutic interventions. In addition to assessing consciousness levels, fNIRS offers unique advantages in tracking hemodynamic changes associated with neuroregulatory treatments, including DBS and SCS. By providing real-time feedback on cortical activation, fNIRS facilitates optimizing therapeutic strategies and supports individualized treatment planning. Continued research addressing its technical and methodological challenges will further establish fNIRS as an indispensable tool in the diagnosis, prognosis, and treatment monitoring of DoC patients.

BACKGROUND:Convergent dynamic functional connectivity studies have demonstrated their potential as a hallmark for capturing the impairments in brain function associated with Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, our understanding of whole-brain dynamic patterns remains limited, which hampers understanding of cognitive impairment and symptomatology in AD and MCI. METHODS:An energy-landscape analysis was conducted to investigate brain dynamics across 7 large-scale networks in 516 normal control participants (NCs), 404 patients with AD, and 441 participants with MCI from a multicenter cohort. RESULTS:This method identified major brain states and quantified their size, duration, and transitions. In AD and MCI, transitions between these major states were excessively frequent, state durations were abnormal, and brain state sizes were enlarged. Furthermore, direct transitions between major states were significantly negatively correlated with cognitive ability and structural characteristics. CONCLUSIONS:This study has revealed aberrant brain dynamics in large-scale networks among patients compared with NCs, suggesting that patients experience less stable states and more frequent transitions. The brain dynamic-cognition and dynamic-structure associations indicate that the dynamics of brain states could serve as a critical biological endophenotype of AD. These findings provide new insights into understanding and addressing brain network dynamics in AD and MCI.

Repetitive transcranial magnetic stimulation (rTMS) emerges as a promising non-invasive neuromodulation technique for the treatment of patients with disorders of consciousness (DOC). The selection of rTMS parameters significantly influences the clinical therapeutic effects. However, the differences in spatiotemporal responsiveness of the brain under different rTMS stimulation frequencies remain unclear. In this pilot study, functional near-infrared spectroscopy (fNIRS) was used to evaluate the spatiotemporal differences in hemodynamic responses elicited by rTMS at different frequencies (1, 5, 10, 15, and 20Hz) over left dorsolateral prefrontal cortex (F3). The results showed that the distribution patterns of the rTMS-evoked hemodynamic responses varied across different frequencies, indicating that rTMS frequency influences the hemodynamic responses in patients with DOC. Specifically, 10Hz rTMS evoked strong positive hemodynamic responses over the frontal cortex, particularly in the right dorsolateral prefrontal cortex (R-DLPFC). Additionally, 20Hz rTMS produced largepositive hemodynamic responses over the motor-related cortex, especially the right premotor cortex (R-PreM) and right primary sensorimotor cortex (PSMC). The current findings suggested that fNIRS can be used as a promising tool for evaluating the effects of rTMS in patients with DOC. Moreover, it provides useful guidance for the personalized design of rTMS parameters in a clinical environment.

The diagnosis and management of disorders of consciousness (DoC) remain a critical challenge in clinical medicine and neuroscience. The key bottleneck is the lack of reliable biomarkers and an incomplete understanding of the pathophysiological mechanisms that underlie DoC. In view of this, a bedside-compatible, multimodal technique based on electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) was utilized to simultaneously capture neuronal oscillations and accompanying hemodynamics, so as to explore neurovascular biomarkers that can effectively discriminate different states of DoC. Resting-state EEG-fNIRS data from 13 regions of interest (ROIs) were acquired and compared across healthy controls (HC), minimally conscious state (MCS), and unresponsive wakefulness syndrome (UWS) groups. Hemodynamics-based functional connectivity and the spectral power of neuronal activity were quantified and subsequently employed to interrogate neurovascular coupling. The results demonstrated significantly stronger neurovascular coupling and beta-band power in premotor and Broca's areas of the MCS group. A multimodal classifier achieved an accuracy of 87.9% in distinguishing between MCS and UWS. The noninvasive, bedside-suitable nature of this tool underscores its potential for routine monitoring and prognostic assessment in DoC, addressing a critical need for accessible and reliable biomarkers in both neurology and intensive-care practice.

BackgroundHypobaric hypoxic conditions encountered at high altitudes can significantly impact the physiological functions of human body. Therefore, accurate and real-time monitoring of physiological characteristics is crucial for the prevention of brain injuries in individuals with acute and chronic high-altitude exposure.MethodsIn this study, a wireless wearable cerebral oximeter (WORTH band) was used for the continuous, real-time monitoring of physiological parameters, including regional cerebral oxygen saturation (rSO2) and heart rate (HR), among subjects with high-altitude exposure.ResultsDuring the high-altitude (from 46 m to 4300 m) expedition task, there was a significant decrease in rSO2 accompanied by a corresponding increase in heart rate as the altitude increased. Additionally, during the long-term (52 days) high-altitude (from 356 m to 4658 m) cycling task, the altitudes were significantly correlated with the rSO2 and SpO2 in the elderly subjects.ConclusionThe current findings indicate that the WORTH band oximeter can serve as a promising instrument for measuring rSO2 at high altitudes. We hope that the insights derived from this study could contribute to the management of cerebral oxygenation for individuals with high-altitude exposure and further expand the existing understanding of brain functional detection at high altitudes.

Significance:The accurate assessment and classification of residual consciousness are crucial for optimizing therapeutic interventions in patients with disorders of consciousness (DOCs). However, there remains an absence of effective and definitive diagnostic methods for DOC in clinical practice. Aim:The primary objective was to investigate the feasibility of utilizing resting state functional near-infrared spectroscopy (rs-fNIRS) for evaluating residual consciousness. The secondary objective was to explore the distinguishing characteristics that are more effective in differentiating between the unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS) and to identify the machine learning model that offers superior classification accuracy. Approach:We utilized rs-fNIRS to evaluate the residual consciousness in patients with DOC. Specifically, rs-fNIRS was used to construct functional brain networks, and graph theory analysis was conducted to quantify the topological differences within these brain networks between MCS and UWS. After that, two classifiers were used to distinguish MCS from UWS. Results:The graph theory results showed that the MCS group ( n = 8 ) exhibited significantly higher global efficiency ( E g ) and smaller characteristic path length ( L p ) than the UWS group ( n = 10 ). The functional connectivity results showed that the correlation within the left occipital cortex (L_OC) was significantly lower in the MCS group than in the UWS group. By using the indicators with significant differences as features for further classification, the accuracy for K -nearest neighbors and linear discriminant analysis classifiers was improved by 0.89 and 0.83, respectively. Conclusions:The resting state functional connectivity and graph theory analysis based on fNIRS has the potential to enhance the classification accuracy, providing valuable insights into the diagnosis of patients with DOC.

BACKGROUND:Alzheimer's disease (AD), which has been identified as the most common type of dementia, presents considerable heterogeneity in its clinical manifestations. Early intervention at the stage of mild cognitive impairment (MCI) holds potential in AD prevention. However, characterizing the heterogeneity of neurobiological abnormalities and identifying MCI subtypes pose significant challenges. METHODS:We constructed sex-specific normative age models of dynamic brain functional networks and mapped the deviations of the brain characteristics for individuals from multiple datasets, including 295 patients with AD, 441 patients with MCI, and 1160 normal control participants. Then, based on these individual deviation patterns, subtypes for both AD and MCI were identified using the clustering method, and their similarities and differences were comprehensively assessed. RESULTS:Individuals with AD and MCI were clustered into 2 subtypes, and these subtypes exhibited significant differences in their intrinsic brain functional phenotypes and spatial atrophy patterns, as well as in disease progression and cognitive decline trajectories. The subtypes with positive deviations in AD and MCI shared similar deviation patterns, as did those with negative deviations. There was a potential transformation of MCI with negative deviation patterns into AD, and participants with MCI had a more severe cognitive decline rate. CONCLUSIONS:In this study, we quantified neurophysiological heterogeneity by analyzing deviation patterns from the dynamic functional connectome normative model and identified disease subtypes of AD and MCI using a comprehensive resting-state functional magnetic resonance imaging multicenter dataset. The findings provide new insights for developing early prevention and personalized treatment strategies for AD.

A novel framework of machine learning--experience learning (EL) is proposed for observing new objects and mastering new skills, which can typically be applied to artificial intelligence robots (AIR) exploring the unknown. Unlike traditional approaches, it is not necessary to prepare a large training sample set prior to model training. Instead, an experience chain is established by continuously observing or stimulating the researched objects and recording these experiences, which is inspired by early human learning behavior. Through continuous observation and attempts, the experience chain is updated and gradually converges toward the actual output probability of the researched object. The current experience unit serves as the basis of EL judgment while past experiences can be discarded using a forget coefficient. The application mode of this framework is illustrated with two simple examples. The cat and dog generator experiment represents the self-exploration for new objects. The virtual basketball machine experiment demonstrates the ability of this method to learn a new skill and to effectively mitigate random interference. By comparison, the similarities and differences between the proposed method and the related algorithms are analyzed. Ultimately, this approach proves valuable in enabling artificial intelligence systems to study and explore the unknown territories.

Treating prolonged disorders of consciousness (pDoC) is challenging. Thus, accurate assessment of residual consciousness in patients with pDoC is important for the management and recovery of patients. Functional near-infrared spectroscopy (fNIRS) can be used to detect brain activity through changes of oxygenated hemoglobin/deoxygenated hemoglobin (HbO/HbR) concentrations changes and has recently gained increasing attention for its potential applications in assessing residual consciousness. However, the number of fNIRS studies assessing residual awareness in patients with pDoC is still limited. In this study, fNIRS was used to evaluate the brain function in 18 patients with pDoC, including 14 vegetative states (VS) and 4 minimally conscious states (MCS), and 15 healthy controls (HC). All participants accepted two types of external stimuli, i.e., active stimulation (motor imagery, MI) and passive stimulation (subject’s own name, SON). The results showed that the mean concentrations of HbO/HbR in the prefrontal cortex of the HC during the passive stimulation were significantly lower than those of the active stimulation, and the fitting slope was high. However, the hemodynamic responses of the patients with pDoC were opposite to those of the HC. Additionally, the mean concentrations of HbO/HbR increased as the level of consciousness decreased during passive stimulation. Our findings suggest that the residual level of consciousness in pDoC patients can be assessed by measuring brain responses to different stimulations using fNIRS. The present study further demonstrates the feasibility and reliability of fNIRS in assessing residual consciousness in patients with pDoC, providing a basis for its expanded clinical application.

Significance:Accurate evaluation of consciousness in patients with prolonged disorders of consciousness (DOC) is critical for designing therapeutic plans, determining rehabilitative services, and predicting prognosis. Effective ways for detecting consciousness in patients with DOC are still needed.Aim:Evaluation of the residual awareness in patients with DOC and investigation of the spatiotemporal differences in the hemodynamic responses between the minimally conscious state (MCS) and the unresponsive wakefulness syndrome (UWS) groups using active command-driven motor imagery (MI) tasks.Approach:In this study, functional near-infrared spectroscopy (fNIRS) was used to measure the changes of hemodynamic responses in 19 patients with DOC (9 MCS and 10 UWS) using active command-driven MI tasks. The characteristics of the hemodynamic responses were extracted to compare the differences between the MCS and UWS groups. Moreover, the correlations between the hemodynamic responses and the clinical behavioral evaluations were also studied.Results:The results showed significant differences in the spatiotemporal distribution of the hemodynamic responses between the MCS and UWS groups. For the patients with MCS, significant increases in task-evoked hemodynamic responses occurred during the "YES" questions of the command-driven MI tasks. Importantly, these changes were significantly correlated with their coma-recovery scale-revised (CRS-R) scores. However, for the patients with UWS, no significant changes of the hemodynamic responses were found. Additionally, the results did not show any statistical correlation between the hemodynamic responses and their CRS-R scores.Conclusions:The fNIRS-based command-driven MI tasks can be used as a promising tool for detecting residual awareness in patients with DOC. We hope that the findings and the active paradigm used in this study will provide useful insights into the diagnosis, therapy, and prognosis of this challenging patient population.

Alzheimer's disease (AD) is a common neurodegeneration disease associated with substantial disruptions in the brain network. However, most studies investigated static resting-state functional connections, while the alteration of dynamic functional connectivity in AD remains largely unknown. This study used group independent component analysis and the sliding-window method to estimate the subject-specific dynamic connectivity states in 1704 individuals from three data sets. Informative inherent states were identified by the multivariate pattern classification method, and classifiers were built to distinguish ADs from normal controls (NCs) and to classify mild cognitive impairment (MCI) patients with informative inherent states similar to ADs or not. In addition, MCI subgroups with heterogeneous functional states were examined in the context of different cognition decline trajectories. Five informative states were identified by feature selection, mainly involving functional connectivity belonging to the default mode network and working memory network. The classifiers discriminating AD and NC achieved the mean area under the receiver operating characteristic curve of 0.87 with leave-one-site-out cross-validation. Alterations in connectivity strength, fluctuation, and inter-synchronization were found in AD and MCIs. Moreover, individuals with MCI were clustered into two subgroups, which had different degrees of atrophy and different trajectories of cognition decline progression. The present study uncovered the alteration of dynamic functional connectivity in AD and highlighted that the dynamic states could be powerful features to discriminate patients from NCs. Furthermore, it demonstrated that these states help to identify MCIs with faster cognition decline and might contribute to the early prevention of AD.

It remains challenging to identify depression accurately due to its biological heterogeneity. As people suffering from depression are associated with functional brain network alterations, we investigated subtypes of patients with first-episode drug-naive (FEDN) depression based on brain network characteristics. This study included data from 91 FEDN patients and 91 matched healthy individuals obtained from the International Big-Data Center for Depression Research. Twenty large-scale functional connectivity networks were computed using group information guided independent component analysis. A multivariate unsupervised normative modeling method was used to identify subtypes of FEDN and their associated networks, focusing on individual-level variability among the patients for quantifying deviations of their brain networks from the normative range. Two patient subtypes were identified with distinctive abnormal functional network patterns, consisting of 10 informative connectivity networks, including the default mode network and frontoparietal network. 16% of patients belonged to subtype I with larger extreme deviations from the normal range and shorter illness duration, while 84% belonged to subtype II with weaker extreme deviations and longer illness duration. Moreover, the structural changes in subtype II patients were more complex than the subtype I patients. Compared with healthy controls, both increased and decreased gray matter (GM) abnormalities were identified in widely distributed brain regions in subtype II patients. In contrast, most abnormalities were decreased GM in subtype I. The informative functional network connectivity patterns gleaned from the imaging data can facilitate the accurate identification of FEDN-MDD subtypes and their associated neurobiological heterogeneity.

Maintenance of adequate blood perfusion and oxygen delivery is essential for cerebral metabolism. Cerebral oximeters based on near-infrared spectroscopy (NIRS) have been used for noninvasive, continuous, real-time monitoring of cerebral oxygen saturation and management of cerebral oxygen adequacy perioperatively and intraoperatively in various clinical situations, such as cardiac surgery, anesthesia, and cerebral auto-regulation. In this study, a portable and modular cerebral tissue oximeter (BRS-1) was designed for real-time detection of regional oxygen saturation over the brain, finger, or other targeted body tissues, as well as for wireless cerebral oxygenation monitoring. The compact and lightweight design of the system makes it easy to use during ambulance transport, in an emergency cart, or in an intensive care unit. The system performance of the BRS-1 oximeter was evaluated and compared with two US FDA-cleared cerebral oximeters during a controlled hypoxia experiment. The results showed that the BRS-1 oximeter can be used for real-time detection of cerebral desaturation with an accuracy similar to the two commercial oximeters. More importantly, the BRS-1 oximeter is capable of capturing cerebral oxygen saturation wirelessly. The BRS-1 cerebral oximeter can provide valuable insights for clinicians for real-time monitoring of cerebral/tissue perfusion and management of patients in prehospital and perioperative periods.

基于数字散斑干涉技术同时测量微小偏摆和俯仰角的方法具有非接触测量、无合作靶镜、高测量分辨力、高测量精度等优点,但在确定相位图的条纹方向环节耗时较长,导致测量速度较慢.为了加快测量速度,对相位处理流程和角度测量算法进行了研究,提出了一种改进方法.该方法直接利用相位图上3个点的相位值来同时确定偏摆和俯仰角,不需要计算条纹方向和进行相应的全局图像处理,减少了算法运行时间,提升了测量速度.该方法不仅保留了原方法的所有优点,而且具有测量模型简单、误差源少、测量速度快的特点.