This study proposes a diffusion-based data augmentation framework optimized for few-channel EEG emotion recognition, addressing the intrinsic data scarcity and information loss inherent in low-cost, portable EEG systems. Using the SEED dataset, we utilized 30-dimensional differential entropy (DE) features extracted from six frontal channels. To effectively model these low-dimensional tabular features, we designed a class-conditional ϵ-prediction Denoising Diffusion Probabilistic Model (DDPM) incorporating a FiLM-ResMLP architecture. Furthermore, we introduced a novel Mean-Guided Guidance Sampling mechanism to explicitly steer generated samples toward class-consistent statistical distributions, thereby enhancing training stability. Experimental results demonstrate that the proposed FiLM-ResMLP based Conditional DDPM achieves a classification accuracy of 57.87%, representing a significant improvement of 4.85%p over the baseline (53.02%). The proposed method consistently outperforms state-of-the-art generative baselines, including Conditional DDPM (FiLM-UNet), Conditional WGAN and VAED2GAN. Notably, our analysis reveals that while the conventional UNet architecture tends to overfit in the low-dimensional feature space, the proposed ResMLP structure exhibits superior robustness and statistical compatibility with tabular EEG data. By demonstrating that data augmentation can effectively compensate for information loss caused by channel reduction, this work presents a viable methodology for overcoming the performance limitations of practical few-channel BCI systems.

Computer Modeling and Simulation (CM&S) is a powerful tool for medical device design and evaluation, complementing traditional methods. Rigorous Verification and Validation (V&V) are essential for ensuring the credibility of CM&S results. However, international guidelines like ASME V&V40 and FDA guidance often lack specific methodologies for developers in statutory law-based regulatory environments like South Korea. This study proposes an enhanced framework for applying CM&S to medical device performance evaluation within the Korean regulatory context. We developed a risk determination matrix to clarify CM&S model risk levels and a corresponding Context of Use (COU) methodology to assist R&D professionals in demonstrating credibility for regulatory submission. These components were designed using a risk-based approach derived from MFDS, ASME, and FDA documentation. The key outputs are: (1) A COU-based model risk matrix that categorizes risks into five levels by evaluating the model's role and the severity and probability of decision-related errors; (2) A COU template structured around three elements: objective, role, and scope. This methodology maintains the risk-based philosophy of ASME V&V40 and FDA guidance while providing concrete steps for domestic regulatory performance testing. It aims to align expectations between researchers and regulators, reducing uncertainty and accelerating decision-making in medical device development.

This study aimed to propose a multiclass cognitive impairment classification framework based on conventional machine learning models using widely adopted clinical cognitive assessment tools, namely the Mini-Mental State Examination (MMSE) and the Clinical Dementia Rating (CDR), and to explore digital biomarkers through explainable artificial intelligence (XAI) techniques. The analysis was conducted using the publicly available Well-documented Alzheimer's Dataset from Kaggle, comprising a total of 436 participants (253 normal, 108 with mild cognitive impairment [MCI], and 75 with dementia). Feature vectors were constructed using age, sex, MMSE scores, and derived age-cognition interaction features. A comparative evaluation of Random Forest, Support Vector Machine (SVM), and XGBoost models under identical conditions demonstrated that the Random Forest model achieved the highest performance, with an accuracy of 0.95 (95% CI: 0.904-0.996). The model also exhibited consistently stable classification performance across ROC-AUC and confusion matrix analyses. Furthermore, SHAP-based explainable analysis revealed that MMSE, MMSE_Age_Ratio, age, and the Age×MMSE interaction were key contributors to cognitive stage prediction. These features effectively captured the progressive nature of cognitive decline and demonstrated strong potential as interpretable and quantitative digital biomarkers. Beyond performance improvement, this study is significant in that it presents a cognitive impairment classification model that simultaneously ensures high predictive accuracy and clinical interpretability.

This pilot study investigated individual differences in postprandial glycemic responses to various exercise modalities performed 30 minutes after meals in overweight young adults using Continuous Glucose Monitoring (CGM). Four participants completed a repeated-measures protocol including a control condition and four exercise modalities: aerobic exercise, resistance training, High-intensity Interval Training (HIIT), and Pilates. Postprandial glucose exposure was quantified as the Area Under the Curve (AUC) over 120 minutes. All exercise conditions demonstrated a consistent tendency to reduce postprandial glucose AUC compared with the control condition, yielding a large effect size (Kendall's W = 0.56), despite the absence of statistical significance (p=.06) due to the small sample size. Notably, the exercise modality associated with the lowest glucose AUC differed across individuals. Furthermore, no significant association was observed between Heart Rate (HR) based exercise intensity and glucose reduction rates (r= -0.14, p=.60). These findings suggest that higher exercise intensity does not uniformly translate to superior postprandial glycemic control and highlight substantial inter-individual variability in metabolic responses. CGM-based monitoring may therefore provide a practical framework for precision exercise prescription tailored to individual glycemic response patterns.

This study aimed to investigate the impact of hearing loss (HL) on language processing abilities and brain activation patterns in adults with HL and normal hearing (NH) using functional near-infrared spectroscopy (fNIRS). Hemodynamic responses in the prefrontal cortex were measured using fNIRS while participants performed phonemic and semantic verbal fluency tasks and a discourse task. Behavioral performance was analyzed in conjunction with fNIRS data to examine group differences in task performance and associated neural effort. The HL group exhibited significantly lower performance on the phonemic fluency task compared to the NH group, accompanied by greater prefrontal activation. This suggests increased cognitive load and the recruitment of additional cognitive resources during phonological processing in individuals with HL. Although group differences in performance on the semantic fluency task were less pronounced, significant differences in deoxyhemoglobin levels were observed, indicating altered neural processing associated with HL. This pattern suggests compensatory neural mechanisms involving functional reorganization and redistribution of cognitive resources. In the discourse task, the HL group showed reduced performance; however, no significant group differences in prefrontal activation were found, implying comparable levels of cognitive effort but reduced processing efficiency in the HL group. The findings show the importance of sustained and adequate auditory experience not only for speech perception, vocabulary development, and articulation but also for mitigating increased cognitive load in individuals with HL. These results provide baseline data to support the development of personalized rehabilitation strategies and educational approaches in communication disorders, highlighting the clinical relevance of investigating the central effects of HL.

This study performed a biomechanical analysis of a new metatarsal sliding osteotomy (MSO) procedure for surgical treatment of metatarsalgia. The MSO procedure is expected to have improved structural stability by using a bone plating system and securing the wide osteotomy plane. We constructed metatarsal and soft-tissue finite element (FE) models and analyzed biomechanical efficacies of the MSO. We applied terminal stance loading conditions to the model, and evaluated plantar pressure, stresses of the bone and implant and interfacial motion. The plantar pressure was decreased as the elevation distance of the procedure increased. The new metatarsal osteotomy was effective in stress relief of the plantar soft-tissue. It was also noted that the structural stability was decreased as the elevation distance increased. In particular, maintaining structural stability is required when the distal fragment is elevated by 4mm or more during the procedure.

Clinical evaluations of energy-based cosmetic devices have predominantly relied on subjective outcomes, resulting in a lack of quantitative, age-stratified data to guide clinical decision-making. This study quantitatively assessed skin responses to sonophoresis (Sono) alone versus low-temperature plasma pretreatment followed by Sono (Plasma+Sono), with clarifying age-related differences relevant to practical application. Sixty-four healthy participants were enrolled and stratified by age (20~30s, n=34; 40~60s, n=30). Within each age group, individuals were evenly assigned to either the Sono-only or Plasma+Sono group, such that each treatment group comprised 32 participants with balanced age distributions. Treatments were administered once weekly for four weeks using a split-face design, with the contralateral side serving as cosmetic-only control. Relative changes in hydration, elasticity, pore condition, and skin sensitivity were calculated based on preand post-treatment measurements. Compared to Sono-only, Plasma+Sono produced significantly greater improvements in hydration and elasticity, indicating an additive effect of plasma pretreatment on ultrasound-mediated outcomes. However, the combined protocol yielded less favorable results in pore-related indices and sensitivity, particularly among younger participants. Within the Sono-only group, older participants consistently demonstrated greater improvements across all skin parameters than younger participants. In the Plasma+Sono group, hydration and elasticity increased across both age groups, whereas pore enlargement and sensitivity escalation were notably more prominent in the younger group. These findings provide objective, age-stratified evidence for both the efficacy and limitations of energy-based skin treatments, emphasizing the importance of age-specific considerations when optimizing cosmetic protocols involving sonophoresis and plasma stimulation.

As the medical device market continues to expand, regulatory requirements to ensure safety and performance have become increasingly stringent, highlighting the need for comparative analysis of operational standards for testing institutions. This study aims to examine and compare the operational standards of medical device testing institutions in Korea and the United States to identify structural differences in their regulatory systems. In Korea, the Ministry of Food and Drug Safety (MFDS) designates clinical trial institutions and testing and inspection institutions in advance under the Medical Device Act. Detailed requirements regarding personnel, facilities and equipment, and documentation are prescribed to ensure a minimum level of institutional competence through a pre-designation regulatory framework. In contrast, the United States operates a post-market-oriented oversight system. Clinical investigations are conducted under 21 CFR Part 812 without prior institutional designation, and regulatory compliance is verified through Institutional Review Board (IRB) review and FDA inspections. Similarly, testing laboratories operate under ISO/IEC 17025-based accreditation and the ASCA program, with regulatory authorities exercising oversight through recognition, auditing, and supervision rather than prior designation. These findings provide policy-relevant insights that may support the establishment of operational standards for newly designated medical device testing institutions and the improvement of existing regulatory frameworks.