Search Results (777)

Vegetation indices (VIs) are widely used as efficient proxies for canopy structure and pigment dynamics, whereas solar-induced chlorophyll fluorescence (SIF) provides a direct indicator of photosynthetic activity. Accurate monitoring of these variables is essential for ecosystem assessment, agricultural management, and satellite product validation. However, long-term field spectroscopy is often influenced by instrument aging and environmental variability, altering radiometric calibration coefficients and introducing uncertainty. In this study, we systematically evaluated the impact of interannual calibration coefficients derived in 2023 and 2024 on six commonly used VIs and SIF. These variables were retrieved from ground-based hyperspectral observations acquired continuously using FLAME-T and QEPRO+ spectrometers over a full rice-growing season. Furthermore, MODIS-like broadband indices were simulated through spectral convolution with sensor response functions. Results revealed clear interannual differences in calibration coefficients, particularly in the blue (450–500 nm) and red-edge (700–800 nm) regions. PRI exhibited the highest sensitivity to calibration changes. SIF also showed pronounced sensitivity due to its intrinsically weak signal and strong channel dependence. Conversely, NDVI and ARVI were highly robust, with the calibration-induced bias of NDVI remaining near zero, benefiting from its normalized formulation and strong near-infrared reflectance. Broadband simulation modified the propagation of uncertainty in an index-dependent manner. Compared with narrowband counterparts, broadband EVI, SAVI, and PRI showed reduced sensitivity, whereas NDVI exhibited a slight sensitivity increase. These findings demonstrate a hierarchy of calibration sensitivity governed by signal strength, spectral band selection, and algorithm design. They emphasize the necessity of frequency-dependent calibration strategies for reliable products in satellite validation studies. Full article

This study aimed to investigate left temporal lobe activity during Japanese reading by examining both task-dependent activity and activity associated with individual differences in reading-related cognitive abilities. Using functional near-infrared spectroscopy (fNIRS), we measured hemodynamic responses around the left temporal lobe during oral reading of words and nonwords in 27 adult female Japanese university students. Furthermore, the relationships of these responses with word rapid reading, Rapid Automatized Naming (RAN), and digit span tasks were analyzed. Uncorrected analysis revealed significant differences between the nonword and word conditions in oxygenated hemoglobin at Channel 12 and deoxygenated hemoglobin at Channel 9; however, these differences did not maintain statistical significance after False Discovery Rate (FDR) correction across the 17 channels. In contrast, activity at Channel 9 was correlated with the time required for word rapid reading and RAN, as well as performance on forward and backward digit span tasks, and these correlations remained significant even after FDR correction. In conclusion, while the extent to which task-dependent phonological decoding load is reflected in left temporal lobe activity warrants further verification, our findings suggest that hemodynamic responses around the left temporal lobe are associated with individual differences in reading speed, naming speed, and phonological short-term/working memory. These findings provide insights by comprehensively capturing neural activity during Japanese reading from the dual perspectives of task-dependent and individual difference-related activities. Full article

This study evaluated the potential of a miniaturized portable near-infrared spectroscopy (NIRS) device for the non-destructive prediction of the physicochemical and functional quality attributes of red berries. A total of 145 samples from three berry species (aronia, haskap and goji), representing different harvest years and ripening stages, were analyzed. Spectra were acquired over the 908–1676 nm range using a MicroNIR™ 1700 OnSite-W spectrophotometer, and partial least squares regression models were developed to predict total soluble solids, moisture content, pH, total phenolic content and antioxidant capacity. The calibration models achieved coefficients of determination in cross-validation (R²CV) ranging from 0.83 to 0.92, with Root Mean Square Error of Cross-Validation (RMSECV) between 0.281 for pH and 2.085 g Trolox kg⁻¹ FW for antioxidant capacity. External validation confirmed the robustness of the models, yielding R²EV values between 0.76 and 0.88 and Root Mean Square Error of Validation (RMSEV) ranging from 0.381 for pH to 2.095% for moisture content. The highest predictive performance was obtained for total soluble solids (R²EV = 0.88; RMSEV = 1.391), followed by moisture content, pH and antioxidant capacity, whereas total phenolic content showed the lowest predictive accuracy (R²EV = 0.76; RMSEV = 1.914 mg GAE g⁻¹ FW). The Residual Prediction Deviation (RPD) and Range Error Ratio (RER) values further supported the practical applicability of the models for approximate quantitative prediction. Overall, these results demonstrate that portable NIRS is a rapid, non-destructive and reliable tool for the integrated assessment of the physicochemical and functional quality attributes of emerging red berry species. Full article

Background: Language disorders and dyslexia are associated with atypical language- and reading-related processing, but findings from functional near-infrared spectroscopy (fNIRS) studies remain heterogeneous. This systematic review and meta-analysis synthesized existing fNIRS evidence, quantified hemodynamic differences relative to controls, and examined whether different outcomes showed different levels of consistency. Methods: PubMed, Embase, and Web of Science were searched through 9 January 2026. Eligible studies included individuals with language disorders or dyslexia assessed using fNIRS, with outcomes reported as oxygenated hemoglobin (HbO), deoxygenated hemoglobin (HbR), β estimates, or other hemoglobin-based measures. Random-effects meta-analyses were performed when sufficient quantitative data were available, with subgroup and sensitivity analyses conducted where feasible. Results: A total of 830 records were identified, of which 10 publications comprising 11 independent samples met the inclusion criteria. HbO was the only outcome to show a statistically significant overall between-group difference, with lower HbO in the experimental groups than in controls (SMD = −0.61, 95% CI [−1.12, −0.11], p = 0.017), although heterogeneity was substantial (I² = 58.6%). In contrast, pooled results for HbR and β were not statistically significant and showed weaker stability across analyses. Subgroup and sensitivity analyses did not provide sufficient evidence to support disorder category, region of interest (ROI), or outcome metric type as stable explanations for effect size variation. Conclusions: Among the included fNIRS measures, HbO showed comparatively more consistent cross-study evidence than HbR or β. However, because the HbO estimate pooled clinically distinct dyslexia and acquired language-disorder studies within a limited evidence base, it should be interpreted as a preliminary cross-diagnostic signal rather than as evidence of a shared disease continuum or common underlying mechanism. Full article

Lycopene liquid beverages are functional products with strong antioxidant activity, but their quality control is hindered by the lack of rapid and simultaneous detection methods for multiple indicators. This study investigated a near-infrared spectroscopy (NIRS)—based approach for the simultaneous quantification of pH, moisture, and lycopene content in lycopene liquid beverages. A combined preprocessing strategy integrating Savitzky-Golay smoothing and standard normal variate (SG-SNV) was optimized via grid search to reduce spectral noise and scattering effects. Model interpretability was further enhanced by SHapley Additive exPlanations (SHAP) analysis. Furthermore, four variable selection algorithms (MWPLS, UVE-SPA, ICO, and CARS) were compared to improve model performance and interpretability. The results showed that SG-SNV preprocessing notably improved the prediction performance for pH (R²_CV = 0.9398, R²_PRE = 0.9624) and lycopene (R²_CV = 0.9860, R²_PRE = 0.9576), while moisture prediction remained at a comparable level (R²cv = 0.9617, R²_PRE = 0.9727). SHAP analysis identified spectral regions that may be associated with each component, providing chemically plausible explanations. For variable selection, the optimal algorithm differed across the three quality indicators. For moisture, CARS achieved the highest prediction performance (R²_PRE = 0.9946), while ICO provided comparable accuracy with slightly better model stability. For pH, UVE–SPA produced the strongest generalization ability (R²_PRE = 0.9705) with only 5 selected variables. For lycopene, none of the variable selection methods improved upon the preprocessing-only model (R²_PRE = 0.9576); MWPLS retained a parsimonious 21-variable model but with reduced predictive accuracy (R²_PRE = 0.8888). This study explores a preliminary rapid and non-destructive NIR analytical approach for the simultaneous detection of multiple quality indicators in lycopene liquid beverages, suggesting the feasibility of chemometric modeling for this application. The results provide a preliminary foundation for future development of rapid multi-indicator analytical methods. However, this work was conducted entirely under controlled laboratory conditions using a small, gradient-designed sample set with no production-line or online validation; it should therefore be regarded as a proof-of-concept feasibility study. Substantial further work—including validation with larger, naturally variable sample cohorts—would be needed before the approach could be considered for practical deployment. Full article

Early detection of autism spectrum disorder (ASD) in young children is essential for timely referral, developmental monitoring, and access to early intervention. However, conventional screening and diagnostic pathways often depend on parent-report instruments, episodic clinical observation, and specialist-administered assessments, which may delay identification during the first years of life. This scoping review maps the methodological landscape of early ASD detection from traditional clinical screening to multimodal artificial intelligence (AI). A structured literature search was conducted across major biomedical, psychological, and engineering databases for studies published between January 2010 and May 2026. After screening and eligibility assessment, 65 evidence sources were included in the qualitative synthesis, with additional methodological guidelines used to support reporting and appraisal. The reviewed evidence shows that early ASD detection is increasingly shifting from single-session clinical assessment toward multidimensional risk characterization. Clinical and behavioral screening tools remain the foundation of early identification, while eye tracking, video-based motor analysis, acoustic and vocal biomarkers, electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), and molecular or genomic indicators provide complementary information across different developmental windows. AI-based methods, including machine learning, deep learning, Transformer architectures, multimodal fusion strategies, and foundation-model-based representation learning, may improve the objective quantification of gaze, movement, vocalization, neural activity, and biological risk. Nevertheless, most AI-assisted systems remain limited by small and heterogeneous datasets, insufficient external validation, population bias, privacy concerns, computational burden, and limited interpretability. This review argues that future early ASD detection systems should be developed as clinician-supervised decision-support tools rather than autonomous diagnostic instruments. Clinically meaningful progress will require robust external validation, privacy-preserving deployment, age-appropriate risk stratification, and intrinsically interpretable architectures that align model outputs with developmental and clinical knowledge. Full article

This pilot study examined sex-related neuromuscular and prefrontal hemodynamic responses across 3 and 9 kg known-weight and weight-misprediction lifting conditions and explored whether these patterns differed by physical activity level (PAL). Thirty adults completed the lifting tasks. Among the unknown-weight trials, only wrong-guess trials were retained, while the known-weight trials served as comparison conditions. The integrated linear mixed-effects model showed a significant Condition × Sex interaction, indicating that sex-related differences varied across lifting conditions. The omnibus condition and sex effects were also significant, whereas PAL and all interactions involving PAL were not significant. Exploratory PAL-stratified analyses showed condition-dependent sex differences within both PAL groups. In the moderate PAL group, women showed higher task-related ΔHbO $\beta$-values than men across multiple channels under the 3 kg wrong-guess condition after false discovery rate correction. These preliminary findings suggest condition-dependent sex-related neuromuscular responses and sex-related differences in prefrontal hemodynamic responses observed under the 3 kg wrong-guess condition. The exploratory subgroup findings require confirmation in larger and more balanced samples. Full article

Functional near-infrared spectroscopy (fNIRS) is a portable, non-invasive tool for studying cortical function in children with neurodevelopmental and neurological disorders. Although fNIRS use is increasing, heterogeneous study paradigms and cortical targets have limited cross-condition comparisons and the identification of shared research priorities. This systematic review maps cortical regions and reporting patterns in five key conditions—autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), cerebral palsy (CP), hypoxic–ischemic encephalopathy (HIE), and epilepsy (Ep)—from January 2015 to December 2025. A systematic search across five databases identified 72 relevant studies meeting PRISMA 2020 criteria, revealing both similarities and differences across disorders. The prefrontal cortex (PFC) was studied in all five conditions (5/5: 100%), making it the most consistently investigated cortical region. The parietal and temporal cortices were studied in 4 of 5 conditions (80%). The frontal cortex had the most regions investigated, while the temporal cortex showed the most consistent coverage across conditions (2.25 conditions per region). Beyond regional preferences, condition-specific patterns were aligned with their disorder phenotypes: social brain networks in ASD, prefrontal executive systems in ADHD, sensorimotor changes in CP, cerebrovascular monitoring in HIE, and state-dependent changes in Ep. Across conditions, researchers found altered prefrontal activity, disrupted connectivity, and compensatory brain responses, supporting broader frameworks. Collectively, these findings identify the PFC as a shared target for transdiagnostic fNIRS investigations while highlighting important gaps in regional coverage and methodological consistency. Despite methodological differences, fNIRS shows promise for identifying both shared and unique brain patterns in pediatric neurodevelopmental and neurological disorders. This review provides a framework for prioritizing cortical targets and guiding future standardized fNIRS research in pediatric populations. Full article

The development of transparent multifunctional coatings capable of combining optical properties with mechanical durability remains a significant challenge in advanced materials engineering. In this study, novel hybrid coatings based on a poly(vinyl butyral)/epoxy resin (PVB/epoxy) matrix reinforced with tungsten disulfide (WS₂) nanostructures were developed and examined for potential application in camouflage protection of glass surfaces. Camouflage aims to reduce the detectability of an object by minimizing the optical contrast between the object and its surrounding environment. For transparent substrates such as glass, this objective is particularly demanding because the transparency must be preserved while reducing unwanted surface reflection and optical signatures over relevant spectral ranges. For this purpose, in this research two types of nanostructures were investigated: fullerene-like nanoparticles (IF-WS₂) and inorganic nanotubes (INT-WS₂). The coatings were fabricated via ultrasonically assisted solution dispersion followed by casting over the glass plates and Teflon molds, and solvent evaporation. Structural, thermal, optical, and mechanical properties were systematically evaluated using SEM, FTIR, DSC, UV-Vis-NIR spectroscopy, gloss measurements, hardness testing, and cavitation wear resistance analysis. The incorporation of WS₂ nanostructures led to improved mechanical performance, with increased hardness and enhanced resistance to cavitation-induced wear. Optical characterization showed moderate reductions in reflectance and controlled transmittance in the visible and near-infrared regions, while overall transparency was maintained. The results indicate that WS₂ nanostructures contribute to both light scattering and absorption, leading to reduced specular reflection and improved optical masking potential. The findings demonstrate that hybrid PVB/epoxy/WS₂ coatings offer a promising approach for designing transparent, mechanically resistant coatings with tunable optical properties, with potential applications in protective glass systems and advanced functional surfaces. Full article

(1) Background: The effects of posterior parietal cortex (PPC)-targeted transcranial direct current stimulation (tDCS) on postural stability and cortical activity remain unclear. Therefore, this study aimed to investigate and compare changes in cortical activity and postural stability before and following tDCS conditions. (2) Methods: Eight right-handed adults completed a baseline assessment followed by three stimulation sessions: left-anodal/right-cathodal (L-A/R-C), left-cathodal/right-anodal (L-C/R-A), and sham on the PPC. The sessions were administered in a randomized Latin square design with a minimum 4-day washout period between each. At baseline and following each tDCS session, cortical activity was measured using functional near-infrared spectroscopy, and postural stability during a tandem stance was assessed utilizing the Balance Error Scoring System (BESS) and a force platform. (3) Results: Compared with the baseline measurements, significant deactivation in the right middle temporal gyrus (MTG) was observed following L-C/R-A stimulation. Furthermore, postural stability measures revealed significantly higher BESS error scores and greater sway length following L-C/R-A stimulation compared to both the baseline and L-A/R-C conditions. (4) Conclusions: Bilateral tDCS over the PPC differentially influences cortical activity and postural control depending on the stimulation polarity. Specifically, L-C/R-A stimulation was associated with impaired visual–vestibular integration and postural stability. These preliminary findings highlight the critical role of interhemispheric parietal balance in posture regulation and suggest that polarity-specific tDCS protocols may be important considerations for the future. Full article

Selective hearing in multi-talker environments remains a central challenge for assistive listening technologies: conventional devices improve audibility but cannot infer which talker the listener intends to follow. This Perspective examines the emerging transition from auditory attention decoding to neuro-steered selective hearing. We argue that a recent real-time intracranial proof-of-principle study marks an important inflection point in this transition, because it shows, under high-fidelity recordings from neurosurgical patients and in controlled two-talker conditions, that decoded neural attention can be coupled online to relative-gain control, thereby improving speech perception and reducing listening effort within that experimental setting. Building on this proof of principle, we argue that the field should move beyond offline decoding accuracy as its primary benchmark and instead treat neuro-steered hearing as a closed-loop translational problem linking neural inference, acoustic scene analysis, and control policy. From this perspective, future benchmarks should integrate device-level control metrics, including latency, effective switch time, false-switch rate and recovery after erroneous updates, with listener-centered outcomes such as speech intelligibility, listening effort, user preference and everyday benefit. Accordingly, we outline a staged roadmap for electroencephalography-based systems, from controlled two-speaker paradigms to scene-aware control and ultimately wearable real-world implementations. We also discuss the complementary roles of magnetoencephalography, functional near-infrared spectroscopy, and functional magnetic resonance imaging in defining the neural mechanisms and design constraints of future systems. Overall, this Perspective frames neuro-steered selective hearing as a systems-level research pathway for evaluating whether neural attention signals can be translated into practical assistive listening technologies. Full article

Background: Maintaining postural stability requires the integration of multisensory information, including visual, vestibular, and somatosensory inputs. This study aimed to investigate the effects of narrowband noise across different frequency bands on static postural balance and cortical activation in healthy adults. Methods: Twenty healthy adults participated in a repeated-measures experiment in which they maintained a tandem stance with eyes closed under four conditions: no sound, low-frequency narrowband noise (500 Hz), mid-frequency narrowband noise (2000 Hz), and high-frequency narrowband noise (5000 Hz). Static balance was assessed using center of pressure parameters, including sway length, ellipse surface, delta X, and delta Y, recorded via a pressure measurement platform. Cortical activation in the premotor cortex (PMC), frontal eye fields (FEF), superior temporal gyrus (STG), and middle temporal gyrus (MTG) was measured simultaneously using functional near-infrared spectroscopy. Results: Mid-frequency narrowband noise significantly improved postural stability, as evidenced by reductions in sway length, ellipse surface, and delta Y compared to the high-frequency and no-sound conditions (p < 0.05). In contrast, high-frequency narrowband noise consistently produced the greatest postural instability, with significantly larger ellipse surface and delta Y values (p < 0.05). Regarding cortical activation, high-frequency stimulation induced significantly greater bilateral activation in the PMC compared to low-frequency stimulation, while all auditory conditions elicited widespread activation across PMC, STG, and MTG. Conclusions: These findings suggest that the frequency characteristics of auditory stimulation exert differential neurophysiological effects on balance control. Mid-frequency narrowband noise may enhance static balance by facilitating sensorimotor integration, whereas high-frequency narrowband noise may induce PMC hyperactivation, potentially contributing to postural instability. Full article

The assessment of consciousness has been shaped largely by research on acquired disorders of consciousness after acute or chronic brain injury, but similar problems of unreliable behavioral expression increasingly arise in neurodegenerative disease. This translational overlap is especially relevant when preserved cognition, awareness, or intentionality cannot be reliably expressed because of severe motor impairment, fluctuating arousal, cognitive decline, aphasia, apraxia, or impaired cooperation. In neurodegenerative disease, degeneration of arousal systems, large-scale brain networks, cognition, and motor pathways may similarly make observable behavior an unreliable measure of awareness. The challenge is not only to determine if a patient responds, but also to ask if residual awareness, intentionality, or covert cognition can still be detected through physiological signals. This review discusses how contemporary modalities reshape this assessment. Electroencephalography has moved from a descriptive measure of background activity to a bedside tool capable of probing event-related responses, network organization, and cortical complexity. Magnetic resonance methods reveal altered connectivity within thalamocortical and default mode network systems, while functional near-infrared spectroscopy adds a portable hemodynamic approach that may be repeated at the bedside and integrated with active paradigms. Brain–computer interfaces provide a translational step by converting neural responses into evidence of command following or, in selected patients, into communication, and artificial intelligence strengthens these approaches by extracting clinically meaningful patterns from complex neural and hemodynamic data. Additionally, autonomic measures, including heart rate variability and baroreflex indices, are considered as auxiliary physiological context for arousal and engagement, and not as direct markers of awareness. Because the most mature evidence for covert awareness and cognitive-motor dissociation comes from acquired disorders of consciousness, this review treats brain injury literature as a methodological foundation instead of as directly interchangeable evidence for neurodegenerative disease. It then examines how these approaches may be adapted to neurodegenerative contexts, especially ALS, severe dementia, Lewy body disease with fluctuating cognition, and conditions in which communication or motor output becomes unreliable. Full article

Background: Central nervous system development is a rapid and highly plastic process during the first years of life. Tactile stimuli have been shown to induce cortical changes, but potential sex-related differences remain unexplored. This study aimed to investigate sex-specific differences in cortical activity and cerebral oxygenation in response to tactile stimulation via body massage. Methods: Four healthy full-term infants (two females and two males), all aged 11 weeks, were included in this prospective exploratory study. Each infant received a standardized 5 min massage protocol. Cortical activity and cerebral oxygenation were assessed using an 8-channel electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) before, during, and after the intervention, with a 5 min pre-intervention resting period used as the baseline. Results: EEG analysis focused on a single spectral band (4 Hz–30 Hz). This range was selected to capture the main cortical oscillations in infants, including theta, alpha, and beta activity, while delta activity below 4 Hz was partially excluded to reduce movement and physiological artifacts. Standard infant EEG bands were considered when defining this range. Data shows for the female subject an average PSD of −6.726 (± −4.075), and for the male subject, −12.594 (± −10.741). Although babies are of the same gestational age, they exhibited distinct basal cortical activity, which prevented comparisons from being made. Nevertheless, massage induced similar activity patterns in all subjects with increased cortical electrical activity in the left parietal region relative to baseline. fNIRS data showed that comparable HbO concentration patterns between participants were observed only during the second minute of recording. Relative to baseline, pre-intervention HbO responses displayed an opposite distribution, and the effects of the intervention differed by sex. The female participant exhibited a slight reduction in activation in the right hemisphere accompanied by a modest increase in the most ventral region of the left hemisphere. Conversely, the male participant showed an inverse response pattern, characterized by a marked increase in right hemispheric activation and a pronounced decrease in the left hemisphere during the intervention period. Conclusions: These preliminary observations suggest the presence of early variations in cortical processing that warrant further investigation in larger samples, although they cannot be considered conclusive. While baseline response patterns differed between participants, both showed increased left parietal activity during tactile stimulation. The inversion of HbO responses between the pre-intervention and intervention phases points to potential sex-related differences in hemodynamic trajectories. Nevertheless, these results remain preliminary, and larger, well-powered studies are required to determine whether these patterns reflect stable, sex-dependent developmental changes. Full article

Decoding mental states from non-invasive neural recordings is central to brain-computer interface research. Multimodal acquisition that combines electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) couples the high temporal resolution of EEG with the spatial specificity of fNIRS, compensating for the individual limitations of each modality. While such hybrid systems achieve strong intra-subject performance, cross-subject generalization remains constrained by inter-individual variability in neural responses. This study introduces the Eigenvalue-Guided Positional Encoding Classification Transformer (EPECT), an architecture that integrates eigenvalue-aware multi-head self-attention with sinusoidal positional encoding to capture both the spectral structure of the learned feature representations and the temporal ordering of multimodal sequences. Stacked one-dimensional convolutions extract local patterns prior to transformer encoding, and global average pooling aggregates the final representation for classification. EPECT was evaluated on two publicly available EEG-fNIRS datasets covering motor imagery (MI), n-back, discrimination/selection response (DSR), and word generation (WG) paradigms under a cross-subject protocol. The model achieved classification accuracies of 97.3%, 96.3%, 98.1%, and 97.9% on the MI, n-back, DSR, and WG tasks, respectively. Ablation studies quantified the contribution of each architectural component, and integrated gradients analysis revealed structured modality-specific attribution patterns aligned with task-relevant cortical regions. Additional experiments with synthetic cortical perturbations demonstrate the sensitivity of EPECT to subtle activity changes, indicating potential utility for tracking neurorehabilitation outcomes in future clinical applications. Full article