Biomedical Physics & Engineering Express

Background: Independent end-to-end verification of adaptive radiotherapy on MR-Linac systems is limited by the lack of patient-specific phantoms able to reproduce imaging and dosimetric properties from CT and MRI scanners. We present a method for automated generation of 4D, patient-specific, multi-material 3D-printable phantoms for quality assurance of adaptive radiotherapy on a 1.5T MR-Linac. Methods: Patient images were automatically segmented using a pretrained deep learning model. The segmented structures were converted into high-resolution 3D meshes and assembled into printable phantoms. A dosimeter holder was inserted at user-defined anatomical locations, with orientation optimized to avoid traversal across heterogeneous tissue interfaces. Physiological motion was incorporated by generating phantoms from images at different timepoints and interpolating deformation fields to create continuous 4D models. Multi-material organs designed by mixing a set of six polymers at various proportions were used to reproduce tissue-specific imaging properties. The properties of material mixtures were evaluated in a clinical CT simulator and a 1.5T MR-Linac. Results: The proposed workflow enables automated generation of anatomically realistic phantoms with several types of embedded dosimeters. A discrete search method was designed for placement and immobilization of OSLD, film, and ion chamber dosimeters. Calibration curves for Hounsfield units were derived through variations in radiopaque material content, while MR signal intensity was modulated by gel and tissue matrix mixtures. Patient-derived abdominal phantoms were fabricated at multiple scales while replicating internal anatomical detail. Multi-dimensional phantom generation enabled continuous representation of motion states with consistent mesh topology across phases. Conclusions: We demonstrate an end-to-end workflow for automated generation of 4D patient-specific phantoms for MR-Linac quality assurance. The method combines realistic anatomy, embedded dosimetry, multimodal imaging properties, and physiological motion within a single fabrication framework. This approachmay enable an improved validation of adaptive radiotherapy workflows in MR-guided treatment devices.

Accurate segmentation of thin-wall organs-at-risk (OARs)-the cochlea, vestibular semicircular canals, internal auditory canal, tympanic cavity, and middle ear-is clinically relevant for head-and-neck radiotherapy planning, yet these small, thin-wall structures remain among the most challenging targets for automated delineation. Dual-frequency feature fusion is a promising direction for boundary-sensitive representation, but under the investigated FP16 FFT-FcaNet setting, we observe an approximately 863-fold activation-scale mismatch between the FFT and FcaNet branches, causing a nominal 5 percent residual coefficient to behave as an approximately 43-fold dominant term. We propose FreqFuseNet, which resolves this mismatch by normalizing the FcaNet branch to the FFT activation scale before residual injection with a fixed low-amplitude coefficient (beta = 0.05), restoring beta as an interpretable 5 percent residual-amplitude coefficient relative to the FFT feature scale. Under a controlled binary per-OAR ROI protocol on the SegRap2023 head-and-neck CT benchmark across 10 clinically prioritized thin-wall OARs, FreqFuseNet achieves Dice of 0.849, HD95 of 0.824 mm, and SDice@1mm of 0.959 in the primary seed, with comparable performance in an independent second seed (Dice 0.843, HD95 0.823 mm). FreqFuseNet yields statistically significant case-level aggregate improvements over 3D U-Net and MedNeXt-S (Wilcoxon p < 0.01 and p < 0.05, respectively), using only 29.7 million parameters versus 414.6 million for the full wavelet baseline.

Recent NCCN guideline highlighted AI-based mammographic risk prediction, but AI-based breast cancer detection remains questionable to translation. One barrier is current models often do not match routine clinical reasoning, which may add decision burden than benefits. In practice, radiologists compare current and prior mammograms while assessing breast density, bilateral symmetry, and lesion laterality. To align AI with this reasoning, we developed MuSTAF, a multi-task spatiotemporal attention fusion model for patient-level breast cancer classification from longitudinal full-field digital mammography. MuSTAF uses up to three recent mammograms, integrates temporal and cross-view information, refines suspicious-region features, and jointly predicts cancer status, breast density, and bilateral symmetry, with a separate laterality classifier for cancer-positive cases. In an internal case-control cohort (n = 351), MuSTAF achieved a cancer classification (AUC=0.84) exceeding all architecture-level baselines and published mammography AI models adapted to the same task (AUC [&le;] 0.81). Simultaneously, it achieved AUCs of 0.83/0.80 for density/laterality assessments, and removing these auxiliary tasks reduced cancer detection performance. On the external CSAW-CC dataset (n = 8,723), model performance improved from 0.72 to 0.88 when restricting cancer cases to those with latest exams within 60 days before diagnosis, showing that temporally distant labels may shift detection evaluation toward risk prediction. Longitudinal analysis further showed that three recent exams outperformed five exams internally (AUC = 0.84 vs 0.80) and externally (0.72 vs 0.66), indicating recent imaging evidence mattered more than remote history. Overall, MuSTAF model improved longitudinal mammographic cancer classification while providing auxiliary outputs, and clarified temporal factors for applying AI to screening detection.

Purpose: RF-induced heating remains a major barrier to MRI access for patients with epicardial cardiac implantable electronic devices (CIEDs). Although ISO/TS 10974 Tier-3 transfer function (TF) methods are established for unbranched leads, no analogous framework exists for bifurcated leads, in which branch asymmetry and inter-branch coupling may substantially alter heating. We developed and validated a cumulative transfer function (cTF) framework to address this gap. Methods: Following ISO/TS 10974 Tier-3 formalism, we measured, calibrated, and validated cTFs for a commercial 35 cm bipolar epicardial lead at 1.5 T. The framework explicitly accounts for branch-specific response and cross-branch coupling. Validation was performed with 24 canonical lead configurations in a homogeneous phantom and, without recalibration, in a heterogeneous anthropomorphic pediatric phantom with clinically derived trajectories. A single-branch TF approximation served as a comparator. The validated cTF was applied to predict RF heating across adult and pediatric human models at multiple imaging landmarks. Results: Compared with the single-branch TF approximation, the cTF reduced prediction error by nearly 70% in the primary validation dataset. In secondary validation, the cTF maintained low error across clinically relevant trajectories and imaging landmarks. In human models, the framework revealed marked anatomy- and landmark-dependent variation in predicted heating for the tested 35 cm lead, with low predicted heating in pediatric models and substantially higher heating in selected adult chest and upper abdominal imaging scenarios. Conclusion: The cTF provides a validated framework for RF-heating assessment of bifurcated leads and substantially improves prediction accuracy over single-branch TF approximations that neglect branch coupling.

Background: Nuclear medicine and radiopharmaceutical development require coordinated radiochemistry, dosimetry, molecular imaging, radiation-safety and clinical decision processes. Current workflows remain fragmented, difficult to audit and poorly standardised for evaluating domain-specific AI support. Methods: We developed RadGuide AI, a nuclear medicine agent built around a traceable data-model-tool loop. Patent, literature and clinical-trial records were converted into 15,596 initial QA items; relevance screening, completeness checks, semantic deduplication and cross-validation retained 5,474 core QA items. MedGemma-27B-Instruct served as the foundation model and was adapted with LoRA. The system incorporated 55 MCP-wrapped tools covering radiopharmaceutical R&D, clinical decision support, imaging analysis and radiation-safety/dosimetry. Evaluation used a locked N=200 benchmark with predefined denominators, leakage control, expert scoring, statistical procedures, factuality audits and tool-execution metrics. Results: RadGuide-LLM achieved 88.5% answer accuracy (177/200; 95% CI, 83.3-92.2%) and a Macro-Average score of 21.5/25 (bootstrap 95% CI, 20.9-22.0), exceeding GPT-4o, DeepSeek-V3.2 and the base MedGemma model in this technical evaluation. Supplementary audits reported guideline compliance, terminology recall, knowledge coverage, tool-routing success and preclinical/phantom dosimetry agreement with explicit denominators and confidence intervals. Interpretation: RadGuide AI converts nuclear medicine queries into auditable retrieval, tool selection, calculation, verification and reporting workflows. The findings support technical feasibility, not definitive patient-level clinical validation; prospective multicentre studies and external benchmark release remain required before clinical deployment.

A test technique for measuring high-frequency transient current components in deep brain tissue is presented. The technique applies a voltage pulse with a high value in dV/dt, generating a corresponding current pulse with high dI/dt that can elicit measurable transient current responses from the electrode/tissue interface and adjacent brain tissue; responses are analyzed in the frequency domain by Fast Fourier Transform at a 200 kHz sampling frequency. The method was motivated by prior evidence that ferritin and neuromelanin in catecholaminergic tissue may support high-frequency conduction properties that have not previously been characterized in vivo. The protocol was applied in 277 measurements across five Sprague Dawley rats at cortical and basal ganglia locations in different locations in the brain. Preliminary spectral results show differences between catecholaminergic regions and cortical tissue that support further development and validation of the method.

Purpose: To evaluate the latest open-weight vision-language models (VLMs) on the Japanese Diagnostic Radiology Board Examination (JDRBE), assessing overall accuracy and the effects of image input, reasoning, and language. Materials and Methods: In this retrospective study, 29 open-weight VLMs from 13 developers, released in or after January 2025, were evaluated on 327 image-bearing questions from four years of the JDRBE, a non-public benchmark with low risk of data leakage. Each question was answered by each model with and without the image(s), under three language conditions and with reasoning enabled and disabled. Accuracy was the primary outcome, and within-model differences were tested with paired bootstrap confidence intervals and sign-flip permutation tests with Benjamini-Hochberg correction. Results: In the Japanese condition with image input and reasoning, the leading models reached 73.7% (gemma-4-31B-it), 73.1% (Qwen3.5-397B-A17B), and 72.1% (Kimi-K2.6). On the 2025 subset, these three models (74.1%-75.5%) scored above the mean accuracy of five newly board-certified radiologists who passed the 2025 examination (72%; range, 65%-83%). Accuracy broadly scaled with model size, although compact gemma-4-31B-it matched larger models. Enabling reasoning improved accuracy in nearly all models and the contribution of image input was larger when reasoning was enabled, particularly in higher-performing models. English prompts generally outperformed Japanese prompts. Conclusion: Several open-weight VLMs, without medical adaptation, performed at or above the mean of newly board-certified radiologists on the JDRBE, with both model size and reasoning contributing. The highest Japanese-language accuracy came from a compact model suitable for parameter-efficient fine-tuning and serving on a single graphics processing unit.

Objective: Preclinical small-animal irradiators such as the FLASH-SARRP can support the advancement of photon-FLASH toward the clinic. This study aimed at characterizing the FLASH-SARRP and established a robust quality assurance (QA) workflow to enable accurate and reproducible preclinical experiments. Approach: Custom 3D-printed spacers were designed to ensure reproducible X-ray tube alignment, sample positioning and mounting of the dosimetric tools. Beam characteristics were evaluated using a combined dosimetric approach. High spatially resolved dose distributions were obtained from Gafchromic films, whereas a plastic scintillating fiber was employed to monitor in real-time the temporal pulse structure and synchronization between the two X-ray tubes. Day-to-day variability of the delivery was evaluated over several sessions. Main results: The FLASH-SARRP achieved dose-rates of around 80 Gy/s when both tubes were used simultaneously and provided a homogeneous irradiation field suitable for small-animal studies. A desynchronization between the two tubes was observed with an average delay of 10 ms, resulting in temporal dose-rate heterogeneity. Additionally, a substantial inter-session variability (~11%) was found, whereas the intra-session variability was relatively low (~4%). Inter-session variability was reduced to 5%, approaching the intra-session variability, by adding Gafchromic films/scintillator-based quality assurance (QA) workflow into the irradiation routine. Significance: This work highlights the importance of temporal dosimetry for preclinical FLASH studies. Additionally, a practical QA framework is proposed integrating real-time monitoring with reference dosimetry. The proposed work enables adaptive dose delivery, thereby enhancing the reproducibility of the irradiations, which is crucial for reliable preclinical studies on the FLASH effect.

Clinical treatment planning of near-infrared (NIR) brain stimulation requires patient-specific light dosimetry to optimize fluence delivery to cortical targets. The gold-standard Monte Carlo (MC) photon transport forward solver is accurate but computationally expensive and non-differentiable for personalized inverse design across subjects. Here, we present a foundation-model (FM)-encoded, differentiable implicit-neural surrogate for the MC solver. A pretrained 3D MRI/CT foundation model, VISTA3D, is domain-adapted to head phantoms with known optical properties to encode the subject anatomy. Next, an implicit neural representation is used to predict light fluence at arbitrary continuous coordinates. This formulation enables off-grid queries and gradients with respect to illumination parameters. Trained with a physics-informed, decade-stratified loss, the surrogate attains R2 {approx} 0.90 on held-out subjects. Ablation results show that the FM benefit is contingent on domain adaptation. Benchmarked against standard learned surrogates, our model is the most accurate in the high-dose region and best on dose-fidelity metrics ({gamma}-index, treated-volume DICE). Finally, gradient-based optimization through the surrogate recovers MC-consistent illumination configurations 50-240 x faster.

Existing interfaces modulate user experience through visual, auditory, and haptic channels, but direct physiological modulation, which programmatically alters a user's internal state, remains largely underexplored. We present a wearable sonophoresis patch that uses low-frequency acoustic stimulation to deliver psychoactive substances transdermally, and evaluate its potential for programmable physiological modulation in HCI. We tested this in a double-blinded study (N=26) delivering 100 mg caffeine versus sham control, recording physiological signals during rest and a sustained attention task (SART). The planned comparison for heart rate standard deviation during rest was significant (HR-SD p=0.025, d=1.48), with the caffeine group showing suppressed HR~SD consistent with sympathetic activation. Mean heart rate at rest was not significant (p=0.365), but exploratory analyses during the cognitive task revealed significant cardiovascular divergence: heart rate (p=0.003) and heart rate standard deviation (p=0.027) both moved in directions consistent with systemic caffeine delivery, with effects emerging within minutes of device activation and a sustained group effect across all task rounds (p<0.001). These results provide indirect evidence that wearable sonophoresis can deliver substances to modulate user physiology, opening the design space for on-skin chemical interfaces that adapt delivery in real time to change the user's physiological state on demand.

Modern cochlear implants (CIs) use pulsatile stimulation to restore hearing for individuals with severe hearing loss. CIs provide robust speech recognition in quiet but poorly represent temporal fine structure (TFS), needed for challenging listening situations. Analog stimulation preserves the acoustic waveform and may better encode TFS, yet it has not been evaluated combined with modern current-focusing strategies. We compared neural responses in the inferior colliculus (IC) evoked by CI stimuli consisting of 100 pulses/s biphasic pulse trains and 100 cycles/s sinusoidal analog stimulation with monopolar, bipolar, and tripolar electrode configurations in urethane-anesthetized guinea pigs. Following cochlear implantation, multiunit activity was recorded from the tonotopic axis of the central nucleus of the IC using 16-channel silicon probes. Detection thresholds, spread of excitation, vector strength, sustained response percentage, and temporal response properties were quantified. Analog stimulation consistently evoked significantly lower activation thresholds than pulsatile stimulation while maintaining comparable or sometimes narrower spatial selectivity across stimulation modes. In contrast, analog stimulation generated lower vector strength, larger tonic response components, and a pronounced level-dependent polarity effect. At low stimulus levels, responses were dominated by the cathodic phase of the sinusoidal waveform, whereas increasing stimulus level responses were elicited by both phases, producing synchronization at twice the stimulus frequency. These findings demonstrate that stimulation waveform strongly influences temporal coding while having relatively little effect on the spatial distribution of neural activation. These results provide a physiological basis for reexamining analog stimulation as an alternative strategy for cochlear implant sound coding.

Viscoelastic characterization of tissue has significant diagnostic value in oncology, as tumor progression alters both elasticity and viscosity in ways that neither property alone can fully capture. Existing acoustic radiation force (ARF)-based methods such as Viscoelastic Response (VisR) ultrasound estimate relative elasticity and viscosity through per-A-line nonlinear model fitting, which is computationally intensive and requires auxiliary simulations to correct elasticity-dependent bias. This work presents VESTA (Machine Learning-Enabled Estimation of ViscoElastic Ratios from On-Axis Spatio-Temporal ARFI Features), a two-stage data-driven pipeline that predicts elasticity ratio (ER) and viscosity ratio (VR) directly from seven normalized ARFI displacement features at the A-line level, without model fitting or compensation. Stage~1 is an MLP classifier that detects inclusion boundaries from normalized peak displacement and negative peak velocity ratios; Stage~2 is a dilated Conv1D regression model that estimates ER and VR along the full axial sequence using the predicted mask alongside displacement features. The pipeline was trained on 500 simulated inclusion scenarios spanning three geometries, five focal depths, two F-numbers, and a broad range of material contrasts. In silico, mean predicted ER and VR were within 12\% of ground truth across all geometries, with performance best when ER and VR were moderate or decoupled. Experimental validation on a chicken breast phantom demonstrated plausible generalization to real tissue heterogeneity. Applied to an in vivo murine 4T1 breast cancer model, the pipeline tracked treatment-related attenuation of mechanical contrast in paclitaxel-treated tumors relative to controls over a 36-day imaging period, supporting its relevance for tumor monitoring.

Short-wave infrared (SWIR) sensors promise to expand the capabilities of optical sensing technologies but the lack of robust data characterising tissue-constituent optical properties in the SWIR makes instrument design challenging. We characterise and evaluate the optical properties of the dominant chromophores in tissue and tissue-mimicking phantoms, from visible to SWIR wavelengths. Using single-integrating sphere systems, we measured the optical properties of single-component chromophores (H2O, haemoglobin, corn oil, synthetic melanin) and multi-component tissues (whole blood, lard), to decouple contributions from optical scattering, H2O absorption and other contributing chromophores; we also characterised commonly-used phantom materials and investigated their potential to mimic soft tissues in the SWIR range using simulations. We provide a consistent dataset of absorption and reduced scattering coefficients that characterise the dominant tissue chromophores from 450 nm out to 1600 nm. These results were shown to be consistent with literature data, where available. We integrate these data into an open-source Python toolkit, SIMPA, for optical modelling and demonstrate soft tissue simulations that can be probed continuously from visible to SWIR wavelengths. Our findings are compared with tissue-mimicking phantoms, highlighting a need for additives for polymer-based phantoms that mimic SWIR water absorption. By providing this open-source dataset, we aim to enable future studies exploring SWIR light-tissue interactions that facilitate rapid assessment and prototyping of next-generation spectroscopy and imaging techniques.

Motor unit (MU) decomposition using high-density surface electromyography (HD-sEMG) has been widely used to characterize MU behavior in neurophysiology and to build neural-machine interfaces for wearable robots. Recently, many open-source software tools for MU decomposition have been made available on GitHub, which could reduce the effort of researchers in the field. However, the consistency among these open-source tools has never been studied, making researchers hesitate to use them. In this study, we collected 7 open-source software tools on GitHub and applied them to decompose MUs from an open-source HD-sEMG dataset (including 11 isometric contraction trials) to investigate the consistency among these tools. To create a comprehensive MU pool for reference, we combined all unique MUs identified by seven tools, visually inspected and removed bad MUs, and manually edited all remaining MU spike trains. Across 7 tools for 11 trials, the number of identified MUs ranges from 167 to 736. The number of valid MUs after expert inspection ranges from 29 to 210, which is 10% to 72% of the reference pool. The rate of agreement between the raw MUSTs and the manually edited MUSTs ranges from 0.86 to 0.94, and the averaged number of edits per MU to correct misalignments ranges from 14 to 39. The results show inconsistency in the implementation and procedures of each tool, which results in an inconsistent number of identified MUs and valid MUs (29 vs 210). In general, a substantial amount of effort is required to process the raw MUSTs from each tool to conduct further research analysis. This study provided a guideline for using open-source software tools for MU decomposition and indicated that it would be beneficial to develop tools to automatically edit the MUSTs.

Accurate interpretation of planar ventilation-perfusion (V/Q) scintigraphy, used for diagnosing pulmonary embolism (PE) based on PIOPED/EANM guidelines, requires objective assessment of mismatched V/Q defects. Manual delineation of V/Q defects is time-consuming, subject to interobserver variability, and rarely performed in practice, limiting standardized reporting and quantification of disease burden. To address these challenges, we evaluated four modern AI models for automated segmentation of vascular perfusion defects in planar V/Q scans and compared their performance to human annotators. We retrospectively identified 2,118 patients who underwent planar V/Q scans at The Ottawa Hospital (June 2019-February 2023). Six standard projections (ANT, POST, LAO, RAO, LPO, RPO) were included. Four 2D neural networks (U-Net, nnU-Net, Swin UNETR, and a Bottleneck Transformer U-Net [BTU-Net]) were trained on 1,313 patients (7,878 projections) and validated on 329 (1,974 projections) using physician-annotated defects. A hold-out test set of 46 high probability patients was used to evaluate segmentation quality, and defect detection accuracy using free-response receiver operating characteristic (FROC) analysis, where BTU-Net was the only model performing on par with human readers, showing robust sensitivity across the entire range of segmentation probabilities. At 1.5 false positives per projection rate (FPPR), BTU-Net outperformed other models with a sensitivity of 0.529 {+/-} 0.026, On a separate hold-out set of low likelihood of disease patients (n=430), the lowest FPPR was 0.08 {+/-} 0.01 for BTU-Net (P<0.0001). BTU-Net enables rapid, consistent, and accurate interpretation of planar V/Q scans. Such tools may enhance diagnostic efficiency, standardize reporting, and support non-expert readers in evaluating PE.

Elephants and other large mammals produce low-frequency vocalizations extending well below the 20 Hz lower limit of human hearing, a regime known as infrasound. These rumbles serve vital social and reproductive functions over distances of several kilometers, yet they are inaudible to human observers and cannot be reproduced by conventional small loudspeakers. We present a complete signal-processing pipeline that renders sub-20 Hz elephant rumbles perceptible through a small loudspeaker by exploiting the missing-fundamental psychoacoustic effect. Butterworth bandpass filters isolate the infrasonic content; a full-wave integrator nonlinear device (NLD) generates the harmonic series required for virtual pitch perception; and a hysteresis-comparator fundamental-frequency estimator normalizes the NLD output. The pipeline was validated on African elephant field recordings and deployed on a credit-card-sized, low-cost single-board computer with an infrasound microphone and a small Bluetooth loudspeaker, demonstrating live operation in the field. The processed output shows a 10 dB to 15 dB elevation in the loudspeakers efficient band during call segments compared with background. The system enables zoo visitors and wildlife observers to perceive elephant rumbles in real time, opening new avenues for behavioral studies and public engagement with animal communication.

Here we introduce OpenEvo, a fully open-source, low-cost turbidostat platform for automated continuous culture and directed evolution experiments. Existing tools are expensive, complex, or lack open-source hardware; OpenEvo addresses this gap. OpenEvo is a complete, fully automated evolution platform with detailed, illustrated construction instructions for beginners, open-source software and firmware, and a single device priced around $300. An optional PC-based version offers enhanced functionality, including remote access, programmable evolution cycles, programmable LED stimulation, and a data visualization tool. OpenEvo can cycle through three types of media for positive, negative, and neutral selection conditions, supporting a wide range of experimental designs. We validate the use of OpenEvo by evolving H. volcanii to grow from 15% to 12% salt over ~150 cycles, ~1,000 hours. Evolved cells grew 36% faster than wild-type at 12% salt. Whole-genome sequencing of adapted cells found SNPs and large deletions. We also demonstrate positive and negative selection using the OpenEvo LEDs to drive optogenetics via a Phytochrome B-based optogenetic tool, with light as the selection stimulus during over 4000 hours of growth. OpenEvo lowers the technical and cost barriers for continuous evolution experiments, serves as a teaching tool, and is designed to grow an open community of users who share modifications.

Virtual reality (VR) interaction remains largely dependent on explicit motor input, limiting seamless and adaptive interaction. This study investigated whether electroencephalography (EEG)-based passive brain-computer interfaces (BCIs), combined with eye gaze, can decode interaction intent directly from its underlying neurophysiological correlates during dynamic VR gameplay. We operationalized interaction intent as comprising two components: affordance-related evaluation, indicating whether an attended object affords interaction, and approach-avoidance evaluation, indicating the directional tendency of interaction toward desirable or undesirable outcomes. Twenty-three participants completed a VR game with two calibration sessions and one online BCI session. Offline analyses showed above-chance decoding of the binary approach-avoidance decision classification across all actionable trials, with a grand-average accuracy of 66.28% across participants. This decoding transferred to online closed-loop gameplay, where grand-average accuracy remained above chance at 69.64%. Category-level analyses further revealed substantial variability in classification separability. For approach-avoidance-related classifications, accuracy reached 80.84% for the most distinct pairing between clearly valenced reward and punishment categories, but dropped to near chance at 59.03% for the more context-dependent pairing with ambiguous motivational valence. Affordance-related classifications between non-actionable and actionable item categories were consistently high, ranging from 77.76% to 83.50%. User Experience questionnaire results showed that, despite limitations leading to perceived loss of control and reduced ease of use, participants found the BCI-based interaction paradigm itself more fun than the controller baseline. To our knowledge, this is the first demonstration of real-time EEG decoding of interaction intent during dynamic VR gameplay, contributing toward intuitive user-adapted interfaces driven by physiological signals in immersive environments.

Designing transcutaneous skeletal muscle oxygenation (SmO2) sensors requires jointly optimizing source--detector geometry and wavelength selection while guaranteeing performance across populations that vary in subcutaneous fat thickness and skin pigmentation. We present a multi-fidelity Bayesian optimization (MFBO) framework that couples Monte Carlo light-transport simulations at two photon-count fidelities to a distributionally robust design objective. An autoregressive Gaussian-process surrogate learns the correlation between inexpensive low-photon-count and accurate high-photon-count simulations, and a cost-aware acquisition function decides both where and at what fidelity to sample. Robustness across the population is enforced with Conditional Value-at-Risk (CVaR) and entropic-risk (ERM) objectives that target worst-case subjects rather than the population average. On a five-layer forearm tissue model with anthropometric variability we find (i) a fidelity regime that is favorable for MFBO where the low-fidelity surrogate is rank-informative (Spearman {rho} = 0.84) but biased, at 100x lower cost; (ii) MFBO attains 23% higher robust sensitivity than a strong high-fidelity single-fidelity baseline at equal budget (p = 0.035), and avoids the optimistic bias that causes low-fidelity-only optimization to collapse when its designs are validated at high fidelity; (iii) CVaR/ERM objectives improve worst-case tail performance by {approx}23% relative to a mean objective without sacrificing average sensitivity; and (iv) discovered designs improve robust tail sensitivity by roughly 3--6x over commercial and heuristic optode layouts, with the largest gains in the high-fat and high-melanin subpopulations. The methodology bridges stochastic light-transport physics with sample-efficient machine-learning optimization and generalizes to cerebral oximetry, photodynamic therapy planning, and wearable physiological monitors.

Older adults face an increased risk of falls that may have severe consequences for their well-being. Routine, accessible clinical screening may help mitigate fall risk through early detection of balance impairments. Portable force plates offer a convenient and practical solution for balance assessment in clinical settings. A new force-plate-based balance measure, the intersection-point-height, has shown particularly promising results in its ability to distinguish between healthy and impaired balance behaviors. However, the intersection-point-height measure requires measurement of shear force during standing, which exhibits magnitudes of less than 0.2% of normal forces (body weight), taxing the dynamic range of most sensor technologies. The ability of existing force plates to measure such low-magnitude shear forces observed during quiet standing is currently unknown. This study presents a force-plate performance assessment method to evaluate shear-force measurement errors and quantify the uncertainty of the intersection-point-height measure. The method was applied to test a commonly used laboratory-grade portable force plate. While the device successfully captured sagittal-plane intersection-point-height at the lowest frequencies, low signal strength prevented precise readings in the frontal plane. Thus, the tested device only marginally met the precision required for quiet-standing analysis, underscoring the critical need for systematic performance validation of portable force plates prior to clinical use. Future efforts should focus on evaluating alternative portable force plates and exploring economical design improvements to enhance shear-force measurement precision.