Neurology

Seizures

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

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Automated Segmentation of Post-Surgical Resection Cavities on MRI in Focal Epilepsy: a MELD Study

Objective Quantitative assessment of extent of tissue resection following epilepsy surgery requires ...

Brain-OF: An Omnifunctional Foundation Model for fMRI, EEG and MEG

Brain foundation models have achieved remarkable advances across a wide range of neuroscience tasks....

Association between Interictal Spike Rate and Seizure Frequency in a Large Epilepsy Cohort

Importance: Tracking and predicting seizure frequency in patients with epilepsy is important for pro...

Towards Translational Sleep Staging: A Cross-Species Deep-Learning Model for Rodent and Human EEG

Study Objectives Automated sleep staging underpins clinical sleep assessment and translational neuro...

Autoregressive Visual Decoding from EEG Signals

Electroencephalogram (EEG) signals have become a popular medium for decoding visual information due ...

RepSPD: Enhancing SPD Manifold Representation in EEGs via Dynamic Graphs

Decoding brain activity from electroencephalography (EEG) is crucial for neuroscience and clinical a...

Hierarchic-EEG2Text: Assessing EEG-To-Text Decoding across Hierarchical Abstraction Levels

An electroencephalogram (EEG) records the spatially averaged electrical activity of neurons in the b...

Making Conformal Predictors Robust in Healthcare Settings: a Case Study on EEG Classification

Quantifying uncertainty in clinical predictions is critical for high-stakes diagnosis tasks. Conform...

Automated epilepsy and seizure type phenotyping with pre-trained language models

Background Epilepsy is a common neurologic disorder characterized by recurrent, unprovoked seizures....

LERD: Latent Event-Relational Dynamics for Neurodegenerative Classification

Alzheimer's disease (AD) alters brain electrophysiology and disrupts multichannel EEG dynamics, maki...

Structured Prototype-Guided Adaptation for EEG Foundation Models

Electroencephalography (EEG) foundation models (EFMs) have achieved strong performance under full fi...

BrainRVQ: A High-Fidelity EEG Foundation Model via Dual-Domain Residual Quantization and Hierarchical Autoregression

Developing foundation models for electroencephalography (EEG) remains challenging due to the signal'...

TMS timed to interictal epileptiform discharges

Interictal epileptiform discharges (IEDs) are pathological hypersynchronous bursts of electrical bra...

Application of Explainable AI in Neuroscience: Enhancing Autism Screening

The main challenges in the life of a child with autism are difficulties in communication, behavior, ...

Detection-Guided Artifact Removal for Clinical EEG: A Deep Learning Framework

Objective: We developed and validated a detection-guided artifact removal framework for clinical ele...

Brain4FMs: A Benchmark of Foundation Models for Electrical Brain Signal

Brain Foundation Models (BFMs) are transforming neuroscience by enabling scalable and transferable l...

Bridging the Compression-Precision Paradox: A Hybrid Architecture for Clinical EEG Report Generation with Guaranteed Measurement Accuracy

Automated EEG monitoring requires clinician-level precision for seizure detection and reporting. Cli...

Pupillometry and Brain Dynamics for Cognitive Load in Working Memory

Cognitive load, the mental effort required during working memory, is central to neuroscience, psycho...

ENIGMA: EEG-to-Image in 15 Minutes Using Less Than 1% of the Parameters

To be practical for real-life applications, models for brain-computer interfaces must be easily and ...

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