This study analyzes ClinicalTrials.gov records to track the rising trend of AI in clinical trials and demonstrates that a hybrid human-AI screening approach is viable but requires clearer reporting of…

EvoBrain proposes a dynamic, cross-task continual learning framework to overcome the limitations of task-specific EEG decoding, enabling unified and scalable brain-computer interfaces.

This paper proposes a multimodal cognitive impairment detection framework using large language models that integrates speech audio and transcripts, achieving a high classification accuracy.

This paper benchmarks five positional encoding strategies for transformer-based EEG foundation models, concluding that the optimal encoding is task-dependent and no single strategy is universally supe…

The paper introduces REST-ASMR, a novel multimodal dataset combining PPG and behavioral responses to ASMR and nature videos, and demonstrates that a deep learning model can accurately predict ASMR tin…

EVA-Net proposes a two-stage framework that uses action videos as semantic priors to achieve strong subject-independent EEG motor decoding, significantly outperforming text-based methods.

The study demonstrates that LLMs exhibit significant, language-driven disparities in medical triage recommendations, recommending emergency care more frequently for English and Arabic prompts, even wh…

This paper presents the SiLIF-DBS controller, a low-power neuromorphic silicon neuron stimulator for adaptive deep brain stimulation (aDBS) of Parkinson's disease, achieving strong beta suppression wi…

DSTAN-Med is a novel dual-channel attention framework that significantly improves False Data Injection (FDI) attack detection in IoMT medical devices by explicitly separating spatial and temporal depe…

The paper introduces AutoMedBench, a novel workflow-aware benchmark that evaluates autonomous medical-AI agents across a five-stage research process, revealing that agents struggle most with validatio…

This paper proposes an anatomically aware frequency-and-structure-guided vision transformer (AA-ViT) for accurate and non-invasive contrast enhanced MRI (CEMRI) synthesis using pre-contrast MRI modali…

The paper designed a minimalist BCMI system to translate EEG-measured emotional valence into adaptive music, but preliminary testing showed that frontal alpha asymmetry was not reliably modulated by i…

AutoForest is an end-to-end system that automatically generates publication-ready forest plots directly from biomedical papers, streamlining the labor-intensive process of meta-analysis.

This paper evaluates the use of EEG Foundation Models for burst suppression detection in ICU EEG data, achieving state-of-the-art performance.

This paper investigates neural activity during five auditory conditions using EEG recordings from a 5-year-old participant, revealing condition-specific modulation of neural oscillatory activity and d…

The paper proposes a multi-dimensional evaluation framework to assess EEG foundation models under realistic low-resource conditions, finding that while these models excel in long-context tasks, their…

The paper developed and validated Quantitative Movement Testing (QMT), a computer vision pipeline that accurately extracts 3D kinematic biomarkers from standard smartphone videos, providing an objecti…

This paper compares different decoding pipelines for motor imagery tasks using EEG data and finds that no single pipeline dominates, emphasizing the need for participant-aware model selection.