IEEE Transactions on Neural Systems and Rehabilitation Engineering

Balance is fundamental to mobility and independence, yet it remains difficult to measure in a way that is both clinically meaningful and feasible for widespread adoption. There is a need for more quantitative, objective, and accessible balance assessments. To this end, we created the Stanford Balance Level Evaluation (StaBLE), a battery of 18 balance-challenging tasks and accompanying digital performance metrics computed from video that aimed to capture widely ranging balance abilities across 180 participants. We validated the StaBLE score against existing clinical scales and found that it correlated with age ({rho} = -0.76, p<0.001), the Activities-Specific Balance Confidence Scale ({rho} = 0.60, p<0.001), a Mini-Balance Evaluation Systems Test task ({tau}=0.55, p<0.001), 4-Stage Balance Test ({tau} = 0.55, p<0.001), and Short Physical Performance Battery ({tau} =0.57, p<0.001). StaBLE overcame ceiling effects in these existing scales and had a different underlying distribution for those identified at risk of falling and not (KS: 0.67, p<0.001). To reduce the time it takes to perform the test, we identified subsets of balance tasks that best predicted the StaBLE score across different populations. Together, our dataset, balance assessment protocol, and performance-based score demonstrate the validity of digital measures of balance.

Background and Objectives: Code-modulated visual evoked potentials (c-VEPs) provide fast and reliable control signals for reactive brain-computer interfaces (rBCIs). However, deployment beyond laboratories remains limited by visually intrusive stimulation, reliance on wet electroencephalography (EEG), and false activations during non-control periods. We introduce a usability-oriented c-VEP rBCI combining low-salience textured stimulation, slow flashing codes, asynchronous evidence accumulation, and semi-dry EEG acquisition. Methods: Thirty participants evaluated the system on a standard screen using an 11-class keypad typing task. Two identical online typing sessions were separated by a 2-min video-viewing period serving as an explicit idle condition. Feasibility was then assessed in virtual reality (VR) through a proof-of-concept shell-game task involving head movements and immersive rendering constraints. Visual comfort, peripheral distraction, and fatigue were assessed subjectively in both conditions. Results: In the keypad task, the system achieved 99.8% accuracy with 33 s of calibration data and a mean decoding time of 4.3 s. No spurious commands occurred during the tested 2-min idle condition. In VR, online accuracy reached 100%, with a mean decoding time of 5.6 s. Participants reported high visual comfort and minimal peripheral distraction in both conditions, although fatigue was higher in VR. Conclusion: The proposed c-VEP rBCI combines short calibration, high accuracy, comfortable stimulation, semi-dry EEG, and asynchronous decision-making. Performance during the explicit idle condition and VR implementation support the feasibility of wearable and practical c-VEP systems for self-paced interaction beyond laboratory environments. These findings encourage evaluation in longer idle periods and real-world tasks.

Objective: The aim of the current study was to evaluate the feasibility of a multi-channel distributed wearable ultrasound (D-WUS) system capable of simultaneously probing dynamic muscle activity across multiple anatomical sites to assess neuromuscular coordination during physical activity. Methods: Twenty NCAA Division I women athletes (8 with prior knee injury) performed countermovement jumps (CMJs) on dual force plates while wearing a four-channel D-WUS system targeting the vastus lateralis (VL) and vastus medialis oblique (VMO) in both limbs. Real-time M-mode ultrasound data were processed using a composite factorization approach integrating nonnegative matrix factorization and principal component analysis (CF-PCA) to extract dominant muscle-contraction patterns. Correlation and timing metrics between VL and VMO were compared across injury status. Results: CMJ performance metrics (jump height, RSImod) did not differ between groups (p > 0.05). However, D-WUS revealed significant differences in muscle coordination in the right limb: VL-VMO correlation (p = 0.029) and timing symmetry (p = 0.019) were reduced in participants with prior knee injury; left-limb differences were not significant. Right-limb timing symmetry was negatively correlated with SMFA score (rho = -0.48, p = 0.033). Functional survey scores (SMFA) also differed significantly (p = 0.001), while VISA-P scores did not. Conclusion: These preliminary findings provide feasibility-level evidence that distributed wearable ultrasound can detect limb-specific neuromuscular coordination differences not reflected in force-plate biomechanics, motivating larger prospective studies to establish reliability and clinical utility. Significance: With further validation, this approach may offer a scalable, objective means of monitoring muscle contraction during rehabilitation.

Reduced visibility is a common contributor to falls in older adults, but the neural processes that support safe locomotion under low lighting are not well established. In this study, 22 young (mean age = 22.5) and 20 older (mean age = 70.9) adults walked along a pathway under bright, ambient and dark lighting conditions while stepping over expected and unexpected obstacles projected on the floor. Mobile electroencephalography (EEG), combined with motion capture, was used to quantify spectral power changes during obstacle crossing preparation and the post-crossing phases, relative to a standing baseline. We identified that reduced lighting conditions elicited a widespread increase in cortical engagement, predominantly in the alpha band, during both preparation and reset phases. Obstacle negotiation recruited a distributed sensorimotor network, with reduced theta-alpha-beta desynchronisation during preparation and a robust post-movement beta rebound during the post-crossing reset phase. Expectation effects were modest, with unexpected obstacles inducing greater transient alpha-beta desynchronisation relative to expected obstacles, suggesting additional cortical resources are recruited under uncertainty. Age effects were minimal and limited to reduced frontal alpha desynchronisation in older adults compared to young adults during the preparation phase, possibly reflecting differences in walking speed rather than differences in neural control. In conclusion, environmental features such as low lighting and obstacle presence exert a stronger influence on cortical dynamics during walking than age and expectation effects alone. This provides a foundation for future work examining how these processes may differ in older adults at increased risk of falls.

Background: High-definition transcranial alternating current stimulation (HD-tACS) is favored by the neurostimulation community for its precision and ability to influence neuronal dynamics. Yet, the exact mechanism by which the underlying brain structures are being affected remains unclear. We believe that the investigation of the aperiodic nature of the electroencephalograph (EEG) could shed light on the modulatory effects of HD-tACS. Methods: We analyzed the EEG of 9 participants during a compensatory tracking task (CTT) in two sessions, each with different HD-tACS protocols. Every session consisted of an initial period of no stimulation, followed by 30 Hz HD-tACS in the left motor (M30) or frontal (F30) cortex. We then isolated the aperiodic component of the EEG and calculated its spectral slope {beta}. Results and Discussion: {beta} decreased during F30 mainly in the right frontal cortex, indicating a shift towards higher frequencies and an increase of the excitatory/inhibitory balance. Additionally, we found that despite the long monotonus task the accuracy of the participants did not decrease, which might be attributed to the ability of both M30 and F30 to sustain attention for prolonged time. Finally, the change of CTT accuracy during the stimulation correlated with the {beta} of specific channels before the stimulation. This indicates the potential of {beta} to be used as a screening biomarker in future studies. In conclusion, we showed the ability of HD-tACS to alter EEG aperiodic dynamics and paved the way for future exploration of such dynamics in the field.

Deep learning models for electroencephalography (EEG) pathology detection achieve competitive accuracy but offer limited interpretability. In this paper, we introduce concept bottleneck models (CBMs) for EEG pathology detection which use clinical EEG biomarkers computed analytically from the signal as a bottleneck to be used for prediction. Those concepts include band power, spectral ratios, alpha peak frequency and amplitude, hemispheric asymmetry and phase locking values. With the backbone based on shallow convolutional neural network (CNN), we compare a black box baseline with two variants of CBMs. The hybrid variant is a classifier receives a residual projection of the backbone features. The pure variant is a CBM where the classifier receives only the predicted concepts. Models are evaluated on abnormality detection and dementia classification followed by concept prediction quality and leakage analysis. Both CBMs approach the baseline on TUAB (82.7-82.9% vs 83.6%) and are 2-5 percentage points lower on CAUEEG (57.9-60.5% vs 62.7%). The pure CBM exhibits concept fidelity concern where the predicted concepts deviate from their ground truth values. Replacing them with their true values causes severe cost on accuracy by 26 and 22 points on TUAB and CAUEEG. The concept fidelity concern is mitigated by increasing the weight of the concept loss to match the ground truth concepts. the mitigation comes with small accuracy cost on TUAB but a relatively larger cost on CAUEEG. The code of the experiments and full logs are available at https://www.github.com/mhmdrdwn/cbm.

Mobile EEG can link neuroimaging to natural behavior, but effects recorded during movement, and sport in particular, are difficult to interpret because neural activity overlaps with signals from movement, muscle activity, and cable movement artifacts. We analyzed the OpenNeuro Real World Table Tennis dataset to test whether peri-hit spectral activity could be analyzed and how it differed between human-opponent and ball-machine play. Data from 25 participants comprising EEG, noise-layers, EMG and motion recordings was analyzed with pre-hit, impact, and post-hit windows. EEG power bands were residualized using trial-level root mean square from noise-layer, EMG, and motion/miscellaneous channels. Spectral parameterization modeled spectra as an aperiodic component plus periodic peaks. The principal short-window model was resolution-aware; sensitivity analyses reduced model complexity, used a 2.0-s peri-hit subwindow (-1.0 to +1.0 s), or disabled periodic fitting. Results showed residualized alpha and beta power was lower during ball-machine play, including pre-hit posterior alpha power. The clearest context difference was a higher ball-machine aperiodic exponent over posterior and parietal sensors, corresponding to a steeper broadband spectral slope. Posterior post-hit means were 0.451 and 0.606 for human-opponent and ball-machine play, respectively. A focal parietal effect persisted across specifications. The results support artifact-informed, resolution-aware spectral analysis as an effective approach for detecting robust spectral differences between naturalistic visuomotor contexts and suggest that EEG may provide useful information about the processes engaged by different practice environments, without establishing a single cognitive or physiological mechanism or demonstrating a training outcome.

With the rapid advancement of clinical neuroscience and Brain-Computer Interfaces (BCIs), there is an increasing demand for convenient, user-friendly EEG recording methods suitable for diverse environments, including mobile and home-based settings. Traditional gel-based EEG systems, while reliable, are inconvenient and time-consuming, whereas dry electrode systems tend to suffer from elevated noise levels. In this study, we investigated a novel methodological manipulation aimed at enhancing dry electrode signal quality: wetting the hair directly with tap water to improve scalp electrode conductivity. To this end, we recruited 22 healthy participants and compared their resting-state (RS) EEG activity across three experimental conditions (dry, semi-dry, and gel) within a single-session design. Specifically, we analyzed electrode impedance, spectral power (SP), and functional connectivity (FC). Our results show that basic hair-wetting substantially improved mean electrode impedance by 75.7% relative to the dry condition, reduced the proportion of bad channels from approximately 40% to 15%, and increased spectral-power similarity with gel recordings by 7.7% during eyes-closed recordings, and improved FC similarity by approximately 56-70% across frequency bands. Although this method does not fully match the signal quality of traditional gel-based systems, it represents a promising compromise, enhancing EEG data quality under suboptimal conditions. This approach offers a practical and non-invasive means to improve EEG signal quality, ultimately expanding real-world applications of EEG.

Flow is a subjective state where tasks feel effortless and rewarding. It has been linked to distinct neural signatures, yet dynamics of these signals remain poorly understood. Here, we investigated spectral dynamics of brain activity in flow. We recorded electroencephalogram (EEG) data at positions AF7, AF8, TP9, and TP10 using a lightweight wearable device worn by ten adult participants while they completed a go-signal task and two stop-signal tasks. Task difficulty was continuously adjusted to induce either a sense of flow or frustration. Time-resolved spectral power and interregional coherence were extracted with continuous wavelet and wavelet coherence transforms; subjective flow was assessed after each block with the short-form Flow-State Scale. Subjective ratings confirmed that the flow and frustration conditions diverged in perception of flow. In the flow block, errors on stop trials coincided with pre-stimulus reductions in right temporoparietal gamma power and frontal beta and gamma power, alongside diminished frontotemporal gamma coherence. Comparison of flow and frustration revealed higher temporal beta power during frustration, consistent with greater cognitive load. Flow may involve finely balanced beta and gamma dynamics, and brief collapses of these rhythms may predict task errors. Spectral dynamics are actionable features for closed-loop engagement monitoring applications.

Marker-based optical motion capture remains the gold standard for kinematic analysis across biomechanical research. However, when tracking dynamic movements, severe occlusions often create missing data and noisy signals that require time-consuming manual cleaning. Existing automated methods usually rely on predefined skeleton models or need massive training datasets for machine learning solutions. This study presents the AMP: an automated marker based motion capture processing pipeline. The AMP is a fully analytical approach that uses hierarchical algorithms including 1) rigid-body constraint, 2) Kalman Filter state estimation and 3) kinematic pattern match to label markers and fill data gaps without manual human intervention. First, the algorithm identifies and removes mislabeling markers before any data scrubbing. During the data cleaning process, our hierarchical algorithm uses a translation-based step to track segments even when there are insufficient donor markers to form a full rigid body. To reconstruct missing marker trajectories, the pipeline initially uses Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) interpolation for short marker gaps. However, the PCHIP creates massive spatial errors during long occlusions, the system automatically switches to rigid-body tracking for gaps over 40 ms. Results showed the pipeline drastically reduced processing time compared to manual human expert methods by 73.5%. In addition, the AMP kept spatial errors within 10 mm of the manual ground truth. By using this combined hierarchical approach to match marker processing accuracy, the pipeline provides a fast, general-purpose tool that fully automates data cleaning and saves hours of manual effort.

Measurements of human movement are critical for evaluating patients with neuromuscular diseases. However, current clinical tools are limited in sensitivity and can fail to detect the full spectrum of movement changes patients may exhibit. These limitations can obscure early signs of decline, delaying interventions that could preserve function and independence. OpenCap, a smartphone application that performs video-based markerless motion capture, can help address this gap by enabling scalable, out-of-lab measurements of human movement. Here, we present a method for both detecting disease and quantifying the severity of impaired movement from smartphone videos of patient motion. We used OpenCap to measure kinematics from 337 individuals with diagnoses spanning 12 rare, genetically-defined neuromuscular diseases and 78 age-matched healthy controls. Our dataset is the largest existing publicly available resource of body segment kinematics for this class of diseases. These data were collected in neuromuscular clinics during regular clinic visits as well as at conferences and community events across the United States. We trained deep learning models on these kinematic time series to differentiate between participants with neuromuscular disease and controls (AUROC = 0.97 [0.93-1.00]). We also developed a movement impairment severity score that correlates with both participant ratings of their own functional abilities ({rho} = -0.81, p < 0.001) and existing clinical tests of movement capabilities such as run speed ({rho} = -0.83, p < 0.001). Our methods for detecting disease and deriving quantitative biomarkers of movement impairment from smartphone videos have the potential to complement clinical assessments, inform screening protocols, and improve clinical trial endpoints.

Background: Depth of anesthesia is routinely monitored using electroencephalographic (EEG) indices such as the Bispectral Index (BIS), which reduce cortical activity to a single numerical real-time series. However, these scalar descriptors fail to capture inter-individual variability, drug-specific EEG signatures, and transitions between brain states. Methods: We analyzed transient EEG patterns during general anesthesia induced by propofol and sevoflurane in 89 patients. Iso-electric suppressions (IES), $\alpha$-suppressions, and spectral power in the $\alpha$ and $\delta$ bands were quantified across controlled concentration plateaus. We compared conventional BIS monitoring with a novel State-Chart representation mapping EEG activity into discrete depth states. Results: Increasing hypnotic concentration produced a consistent shift from $\alpha$-dominant to $\delta$-dominant activity and a reduction in maximal $\alpha$ frequency. IES occurred predominantly at high propofol concentrations and remained infrequent under sevoflurane. Compared with BIS, the State-Chart showed a marked reduction in temporal variability (0.5 $\pm$ 0.6 vs 4.0 $\pm$ 2.9; $p<10^{-90}$). Importantly, our State-Chart separates stable from transition regimes and discriminates identical concentration levels. Statistics computed over prior dosing (ascending vs descending), reveals refined clustering not captured by BIS. Conclusions: Anesthesia depth drugs concentrations vs EEG effects is more accurately described by structured EEG state transitions than by a single numerical index. The State-Chart provides a stable, physiologically interpretable representation of brain activity and reveals transition states preceding deep cortical suppression, supporting individualized and predictive monitoring of anesthesia.

Markerless motion capture offers a practical approach to biomechanical assessment in clinical populations outside of controlled laboratory environments. The purpose of this study was to evaluate the utility of markerless motion capture for quantifying static postural control impairment in individuals with stroke. Participants with subacute stroke (n=26) and healthy controls (n=171) performed a 30-second quiet standing task across three data collection sites in Kingston, Ontario, Canada. Video data were processed using Theia3D, and pelvis center of mass (COM) trajectories were extracted. Five discrete COM parameters were computed and transformed into Z-scores using normative models developed from the healthy population. An overall Task Score was calculated by aggregating parameter Z-scores. Forty-six percent of participants with stroke were identified as impaired on the task (Task Score > 95th percentile of the healthy distribution). At the parameter level, impairments were more prevalent in the mediolateral (ML) than anteroposterior (AP) direction (root-mean-square distance: 50% impaired ML vs. 12% impaired AP; mean velocity: 46% impaired ML vs. 35% impaired AP). Task scores and several task parameters demonstrated significant correlations with Berg Balance Scale scores (r = -0.64 to -0.81) and Functional Independence Measure Motor Sub-Scale scores (r = -0.62 to -0.77), with poorer performance associated with lower clinical scores. These findings demonstrate the utility of markerless motion capture for quantifying static postural control impairment following stroke. Future work should extend this framework to evaluate impairment in more dynamic postural control and gait tasks.

Background: Safely walking through complex environments requires the use of visual information to plan and regulate steps, while managing competing cognitive demands. Age-related changes in visual control of gait and executive function may contribute to unsafe stepping behaviour, particularly under cognitive load. Route previewing may support visual motor planning, but its effects under dual task conditions in older adults are not well understood. Research Question: Does cognitive dual tasking influence gait and gaze behaviour during adaptive walking in older adults, and does previewing the walking route before gait initiation mitigate these effects? Methods: Thirty nine community dwelling older adults (Mage=73.4 years, SD=8.1; 14 male) completed four adaptive walking conditions, combining single and dual-task walking with and without previewing. Per condition, participants completed six trials across different 8-m long coloured walking routes. Gait characteristics were assessed using an instrumented walkway and video-analysis, while gaze behaviour was recorded using mobile eye tracking. Cognitive load was manipulated using a serial subtraction task. Gait and gaze outcomes were evaluated using linear mixed effects models. Results: Dual task performance significantly increased stepping errors, cross-steps, and double limb support, while reducing stride length (p < .001). It also significantly increased the number and duration of task irrelevant fixations (p < .001). After previewing, participants exhibited more efficient gaze behaviour, as evidenced by looking further ahead (larger forward gaze transitions) and fewer irrelevant gaze fixations (p < .002). Significant Task-by-Preview interactions revealed that previewing mitigated the negative effects of dual-tasking on stepping errors and cross steps (p < .001). Significance: Route previewing prior to gait initiation attenuates negative effects of cognitive load on adaptive gait in older adults, likely by supporting visual motor planning. These findings highlight the potential value of simple visual planning interventions for improving adaptive gait in older adults.

Dexterous hand control is essential for daily activities and a top restoration priority for people with tetraplegia, yet neurophysiological datasets suitable for studying and decoding naturalistic hand behavior remain scarce. Most available recordings constrain movements to a small number of degrees of freedom or cover only a single stereotypical grasp-and-hold structure. Moreover, previous brain-computer interface (BCI) studies have largely relied on signals from a single cortical area, even though dexterous hand control is distributed across an interconnected frontoparietal network. To address these limitations, we designed a paradigm that elicits rich hand movements across 40 distinct hand-object interaction conditions. We recorded simultaneous frontoparietal activity with hand kinematics from a rhesus macaque performing them. Neural activity was recorded from the primary motor cortex, anterior intraparietal area, and ventral premotor cortex using microelectrode arrays spanning 1,920 channels, while a multi-camera markerless motion-capture system acquired video from which kinematics were extracted offline. The dataset comprises data from 20 sessions, including processed neural features, synchronized 3D coordinates of hand and elbow keypoints, and trial-level metadata including task conditions and event timing. We further provide technical validation, including a neural decoding benchmark with multiple algorithms for continuous keypoint coordination reconstruction. We found that cross-day and cross-task decoding performance varied among algorithms. Variation in channel availability leads to different performance degradation curves in different algorithms. This task-rich dataset offers a valuable resource for investigating the neural mechanisms of dexterous hand control and for advancing the performance of motor BCIs.

Conventional clinical assessment of upper limb kinematics relies on goniometry or visual estimation, methods that provide limited information on multi-segment 3D kinematics and can have poor inter-rater reliability. Inertial measurement units (IMUs) represent a viable alternative; however, extracting anatomically meaningful joint angles from skin-mounted sensors remains challenging owing to sensor-to-segment misalignment, soft-tissue artefact, and suboptimal orientation estimation under dynamic conditions. This study developed and validated a multi-segment IMU-based framework for upper limb kinematic assessment, addressing three stages of the measurement pipeline: (i) sensor fusion gain optimisation as a function of task mean segment angular velocity; (ii) accuracy of an anatomical landmark registration-based alignment method; and (iii) comparative evaluation of landmark-based alignment against manual and neutral-pose alignment approaches. Twenty participants with typical shoulder function performed clinical and functional upper limb tasks, recorded simultaneously with an optoelectronic motion capture system and six IMUs tracking the thorax, clavicle, scapula, humerus, forearm, and hand. Orientation estimation used a gradient-descent complementary filter with velocity-dependent gain modulation. Sensor-to-segment alignment was achieved via manual, neutral-pose, and landmark registration methods. Mean sensor orientation errors ranged from 1.5 to 3 degrees across segments/tasks and increased with angular velocity. Landmark registration produced substantially lower joint angle errors than manual and neutral-pose alignment across all joints (root mean square error range: 3.7-7.8 degrees; manual: 8.3-24.2 degrees; neutral: 15.5-35.5 degrees). These results indicate that IMU-derived kinematic accuracy is context-dependent, varying with joint and movement type, suggesting that accuracy should be established for the specific movements and joints relevant to each clinical application.

Introduction: Knee muscle coactivation is elevated after anterior cruciate ligament reconstruction (ACLR), contributing to altered joint loading and post-traumatic osteoarthritis. Existing methods average coactivation within predefined gait phases and require priori designation of muscles roles, limiting the resolution for when differences occur. Objective: Characterize knee joint coactivation and dynamics throughout gait in individuals with ACLR using statistical parametric mapping. Methods: Ten individuals with unilateral ACLR and ten healthy matched controls performed overground walking at their preferred speed, while kinematics, kinetics, and electromyography of the quadriceps, hamstring, and gastrocnemius muscles were recorded. Coactivation was quantified using time-varying, multi-muscle coactivation, and was compared using a one-tailed statistical parametric mapping (SPM) t-test. Knee joint angle, angular speed, and moment were compared using SPM Hotelling's T2 tests with Bonferroni-adjusted post hoc comparisons (= 0.05). Results: Coactivation was greater in the ACLR group at 0.01%-6.14%, 35.23%-40.94%, 69.73-71.99%, 72.12-77.31%, 77.89-78.05%, and 94.63%-95.87% (p < 0.0001) of stride. The ACLR group also demonstrated greater knee internal-external rotation angle at 0.01%-47.34%, 52.14%-61.87%, and 66.47%-100% (p <0.0001) and lower knee flexion angle at 55.79%-65.93% (p = 0.004) of stride, than healthy controls. Finally, a smaller knee flexion-extension moment was observed in the ACLR group at 57.43%-62.18% (p = 0.005) of stride. Conclusions: Coactivation after ACLR is elevated at loading response, late mid-stance, terminal stance and early push-off phase. Greater coactivation was observed during weight-acceptance phases, whereas lower knee moments characterized initial swing. These alterations are localized rather than uniformly distributed, supporting phase-targeted rehabilitation.

Purpose: To compare the performance of three large language models (LLMs), ChatGPT-o1, Grok 3, and Gemini 2.0 Pro, on the American College of Prosthodontists' National Prosthodontic Resident Examination (NPRE). Materials and Methods: Official NPRE examinations from years 2015-2024 were retrieved from the American College of Prosthodontists website and used to prompt each LLM. Paid access tiers and advanced reasoning were enabled. Responses from LLMs were recorded and compared against the official answer keys for accuracy, variability across years, and consistency of responses. Duplicate questions were retained for the primary analysis and removed in a sensitivity analysis. Accuracy (percent correct) was compared using generalized estimating equations binary logistic regression (primary); Cochran's Q with Bonferroni-adjusted McNemar tests (sensitivity). Year-to-year variability (SD of annual accuracy) was compared using weighted Levene tests. Consistency across duplicate items was quantified as the percentage of perfect matches and compared using Cochran's Q with Bonferroni-adjusted McNemar tests. Results: Across 1,487 questions, overall accuracy differed among models (global P < 0.001). ChatGPT-o1 achieved 78.4% correct, Grok 3 76.4%, and Gemini 2.0 Pro 73.2%; ChatGPT-o1 and Grok 3's accuracy significantly exceeded Gemini 2.0 Pro (both P [&le;] 0.016), with no significant difference between ChatGPT-o1 and Grok 3 (P = 0.099). After removing duplicates (n = 1,200), results were similar: ChatGPT-o1, Grok 3, and Gemini 2.0 Pro were 77.1%, 76.6%, and 71.5% correct, respectively; ChatGPT-o1 and Grok 3's accuracy was significantly higher than Gemini 2.0 Pro (both P < 0.001). Variability across years did not differ significantly (P [&ge;] 0.097). Grok 3 showed significantly higher perfect-match consistency (79.2%) than ChatGPT-o1 (69.4%) and Gemini 2.0 Pro (64.5%) (P [&le;] 0.010). Conclusions: Contemporary LLMs showed 70-80% accuracy on postgraduate prosthodontic multiple-choice content. While promising for educational support, residual error and inconsistency warrant human oversight and transparent methods reporting before curricular adoption.

Aims: Our primary aim was to determine the immediate effects of using a supported stepping device on the real-time locomotor behavior of pre-walking infants with Down syndrome (DS). Our secondary aim was to characterize the role of the caregiver in facilitating infant locomotor behavior by quantifying infant-initiated and caregiver-initiated walker movement, and to test whether walker movement encourages stepping behavior. Methods: Pre-walking infants with DS played with their caregiver in two conditions--with and without assistance from a wheeled suspension walker. To examine changes across development, we recorded infants across one to four sessions from 10 to 19 months of age. Results: Across developmental stages, we found that suspension walkers robustly increased infants' locomotor behavior: The walker increased upright time, percent time stepping, stepping rate, upright bout rate, percent time locomoting, and total bout rate. Most walker movement was initiated by caregivers, and infants stepped more while the walker was moving compared to stationary. Conclusions: Even without practice, pre-walking infants with DS were able to take advantage of the suspension walker to increase their locomotor behavior. Caregivers facilitated infants' activity by moving the walker. Future work will examine the potential of overground suspension walkers for home-based intervention.

Black participants remain underrepresented in electroencephalography (EEG) research, partly because current EEG devices work poorly with dense, coily Type 4 Afro-textured hair. Research-grade EEG typically wires electrodes on a cap, and therefore the electrodes often fail to reach the scalp through dense, Afro-textured hair. Protocols that ask Black participants to unbraid or restyle protective hairstyles add burdens that discourage enrollment in research studies. To address this, we designed and tested 3D-printed attachments that fit onto an EEG electrode and anchored it directly in the hair, using the hairstyle itself as the mounting structure. We fabricated three design configurations for natural Afro-textured hair, cornrows/braids, and twists/microlocs. Seventeen healthy adults with Type 4a-4c hair used electrode attachments that matched their hairstyle. During a single visit, EEG was recorded with 16 conventional cap electrodes defined by the international 10-20 system and with 16 electrodes using novel electrode attachments anchored directly to their hair. Participants completed resting and walking trials with each system. Electrode-to-scalp distance and gel volume were measured during setup. Electrode disconnection, impedance, signal quality (i.e. peak alpha power and aperiodic (1/f) component), and comfort level were measured throughout the data collection. Compared with the conventional cap, the novel attachments reduced electrode-to-scalp distance, gel use, and impedance. Twenty percent of the conventional cap electrodes failed to connect to the scalp compared with 0.1% of novel electrodes. Impedance rose across trials for the conventional cap, whereas the novel attachments showed a smaller increase and maintained lower impedance throughout. Signal quality was significantly better using the novel attachments, while comfort level did not differ between systems. In conclusion, hair-anchored electrode attachment may present a feasible alternative to conventional cap-based EEG for people with Type 4 hair, yielding more usable channels and better signal quality, and supporting more inclusive EEG acquisition.