Neurology

STEAM:ASpatio-TEmporal Alignment Mixture-of-Experts Model with Hierarchical Pre-training for EEG Decoding

Brain-computer interfaces (BCIs) have been widely used in motor rehabilitation, disease diagnosis, and other neural engineering scenarios. However, conventional neural signal decoding algorithms often suffer from limited generalizability and high adaptation costs, motivating recent interest in BCI foundation models. Existing approaches still struggle to jointly achieve general transferability, acc...

Deep Learning-Based Estimation of Ground Reaction Forces in Parkinsonian Gait Using an Optimized Set of IMU Data

Accurate gait analysis in Parkinson's disease (PD) typically relies on laboratory-based systems to capture biomechanical data, such as ground reaction forces (GRFs). Estimating GRFs using inertial measurement units (IMUs) provides a feasible alternative. However, this approach remains challenging in pathological gait like PD due to its high variability and complexity. Moreover, existing monitoring...

Rethinking PPG-based Sleep Staging: Datasets, Metrics, and Benchmarks

Automated sleep staging assigns discrete stage labels to successive time epochs throughout an overnight recording; conventionally each window spans at...

CORTIVA: Candidate-Score Fusion of Complementary Visual Teachers for EEG- and MEG-to-Image Retrieval

Decoding visual experience from non-invasive brain activity is central to neuroscience and brain-computer interfaces. Functional magnetic resonance im...

RADAR-WMH: Relaxometry And Diffusion Analysis beyond Radiologically defined WMH

Background: White matter hyperintensities (WMH) represent the most visible manifestation of cerebral small vessel disease and of white matter patholog...

A Neurosymbolic Approach for Explainable Early Diagnosis of Alzheimer's Disease

Identifying reliable Alzheimer's disease (AD) markers typically requires manual, labor-intensive transcription and expert analysis, limiting its scale...

Localising the epileptogenic zone from single-pulse electrical stimulation responses using cross-trial attention

Background: Analysis of SPES responses often relies on averaging repeated stimulation trials to improve signal quality. However, this may obscure clin...

Metabolic brain networks switch between a sparsely connected baseline and highly integrated states to support cognition

The human brain achieves cognitive flexibility by rapidly switching between large-scale functional network states. While network state switching is as...

Technological Advances in Detecting and Managing Cognitive Impairment in Older Adults: Trends, Challenges, and Future Directions

As populations age, cognitive decline from mild cognitive impairment (MCI) to dementia is a defining health challenge of the coming decades, yet routi...

Do Medical Foundation Models Generalize on the African Brain?

Medical foundation models (FMs) are increasingly used for brain MRI analysis. However, their evaluation remains dominated by high-resource datasets, l...

Multiscale Temporal Processing Supports Sound Recognition under Causal Constraints

The auditory system operates under a fundamental computational constraint: at any moment, it has access only to past and present acoustic information....

EEG-EditBench: Probing Visual Information in EEG-Image Retrieval Models with Controlled Image Edits

Recent EEG-to-image retrieval models have achieved strong performance in identifying viewed images from semantically diverse candidates. Yet such succ...

Secure Aggregation for Privacy-Preserving Federated Learning on Clinical EEG Data

Federated learning enables multiple institutions to train shared models without exchanging raw clinical EEG data, but it does not fully prevent privac...

Rethinking EEG-Based Disease Diagnosis: Decoupling Instance Representation Learning from Subject-Level Supervision

EEG-based disease diagnosis requires one prediction per subject, yet common pipelines segment recordings into short instances, inherit the subject lab...

Generalizability of EEG-Based Dementia Classifiers: A Multicenter study of Alzheimer, MCI, and FTD

EEG-based machine learning shows promise for neurodegenerative disease classification, but robustness to sample imbalance, center heterogeneity, and v...

Fine-Grained Emotional Characterization of Dementia Caregivers in Online Support Communities Using Large Language Models

Background: Dementia caregiving carries substantial emotional and psychological consequences, but most evidence comes from structured surveys and inte...

An Attention-Based Framework for Alzheimers Disease Classification Using Resting-State fMRI

Accurate identification of Alzheimers disease (AD) using resting-state functional magnetic resonance imaging (rs-fMRI) remains challenging due to the ...

Balanced Soft mixture-of-expert model for Glaucoma Detection

Glaucoma is a group of eye diseases that damage the optic nerve, often caused by elevated intraocular pressure. It is a leading cause of irreversible ...

Are the High-weight Neurons the Important Ones in Image Classification Neural Networks?

As neural network models for image classification advance, neurons play critical roles in pruning, backdoor defense, and interpretability. Yet existin...

Dual foundation models for accelerometry predict future health

Wrist accelerometers are ubiquitous and capture activity, sleep, and cardiorespiratory motion, but how this relates to future disease across the pheno...