Neurology

Seizures

Latest AI and machine learning research in seizures for healthcare professionals.

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Modeling Spatiotemporal Neural Frames for High Resolution Brain Dynamic

Capturing dynamic spatiotemporal neural activity is essential for understanding large-scale brain me...

AI Generalisation Gap In Comorbid Sleep Disorder Staging

Accurate sleep staging is essential for diagnosing OSA and hypopnea in stroke patients. Although PSG...

Learning Cross-Joint Attention for Generalizable Video-Based Seizure Detection

Automated seizure detection from long-term clinical videos can substantially reduce manual review ti...

A Clinical Guideline-Grounded Hybrid Agentic Framework for Holistic Epilepsy Management.

Epilepsy is a chronic neurological disorder requiring multi-faceted management, including seizure de...

Toward High-Fidelity Visual Reconstruction: From EEG-Based Conditioned Generation to Joint-Modal Guided Rebuilding

Human visual reconstruction aims to reconstruct fine-grained visual stimuli based on subject-provide...

EEG-based classification models reveal differential neural processing of words and images

Machine learning methods employing neuroimaging data are useful for monitoring the activation of neu...

Laya: A LeJEPA Approach to EEG via Latent Prediction over Reconstruction

Electroencephalography (EEG) is a widely used tool for studying brain function, with applications in...

Data-Local Autonomous LLM-Guided Neural Architecture Search for Multiclass Multimodal Time-Series Classification

Applying machine learning to sensitive time-series data is often bottlenecked by the iteration loop:...

Interpretable Classification of Time Series Using Euler Characteristic Surfaces

Persistent homology (PH) -- the conventional method in topological data analysis -- is computational...

CognitionCapturerPro: Towards High-Fidelity Visual Decoding from EEG/MEG via Multi-modal Information and Asymmetric Alignment

Visual stimuli reconstruction from EEG remains challenging due to fidelity loss and representation s...

Explainable AI Using Inherently Interpretable Components for Wearable-based Health Monitoring

The use of wearables in medicine and wellness, enabled by AI-based models, offers tremendous potenti...

Forecasting Epileptic Seizures from Contactless Camera via Cross-Species Transfer Learning

Epileptic seizure forecasting is a clinically important yet challenging problem in epilepsy research...

LAtte: Hyperbolic Lorentz Attention for Cross-Subject EEG Classification

Electroencephalogram (EEG) classification is critical for applications ranging from medical diagnost...

Characterizing EEG Spectro-Temporal Variability Signatures in Alzheimer's and Parkinson's Disease

We present an EEG-based approach to characterize disease-related spectro-temporal signatures in Alzh...

Machine-Learning-Based spike marking in signal and source space EEG from a patient with focal epilepsy

Accurate detection of interictal epileptiform discharges (IEDs) in electroencephalography (EEG) play...

invertmeeg: A Unified Python Library and Benchmark for 112 M/EEG Inverse Solvers

Magnetoencephalography (MEG) and electroencephalography (EEG) source imaging requires solving an ill...

Exploring sex-related Biases in Deep Learning Models for Motor Imagery Brain-Computer Interfaces

Motor imagery (MI) brain-computer interfaces (BCIs) are promising technologies for neurorehabilitati...

Exploring Electroencephalography for Chronic Pain Biomarkers: A Large-Scale Benchmark of Data- and Hypothesis-Driven Models

Resting-state electroencephalography (EEG) has been proposed as a scalable source of biomarkers for ...

Standing on the Shoulders of Giants: Rethinking EEG Foundation Model Pretraining via Multi-Teacher Distillation

Pretraining for electroencephalogram (EEG) foundation models has predominantly relied on self-superv...

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