Latest AI and machine learning research in seizures for healthcare professionals.
Study Objectives Automated sleep staging underpins clinical sleep assessment and translational neuroscience, yet most data analyses work addresses human and animal data separately. We tested whether a seizure-oriented machine learning framework can be repurposed for three-state sleep staging in humans and rats, and whether models trained solely on rodent data can be applied directly to human recor...
Electroencephalogram (EEG) signals have become a popular medium for decoding visual information due to their cost-effectiveness and high temporal resolution. However, current approaches face significant challenges in bridging the modality gap between EEG and image data. These methods typically rely on complex adaptation processes involving multiple stages, making it hard to maintain consistency an...
Decoding brain activity from electroencephalography (EEG) is crucial for neuroscience and clinical applications. Among recent advances in deep learnin...
An electroencephalogram (EEG) records the spatially averaged electrical activity of neurons in the brain, measured from the human scalp. Prior studies...
Quantifying uncertainty in clinical predictions is critical for high-stakes diagnosis tasks. Conformal prediction offers a principled approach by prov...
Background Epilepsy is a common neurologic disorder characterized by recurrent, unprovoked seizures. Epilepsy manifests as different seizure types and...
Alzheimer's disease (AD) alters brain electrophysiology and disrupts multichannel EEG dynamics, making accurate and clinically useful EEG-based diagno...
Electroencephalography (EEG) foundation models (EFMs) have achieved strong performance under full fine-tuning but exhibit poor generalization when sub...
Developing foundation models for electroencephalography (EEG) remains challenging due to the signal's low signal-to-noise ratio and complex spectro-te...
Interictal epileptiform discharges (IEDs) are pathological hypersynchronous bursts of electrical brain activity that occur between seizures in patient...
The main challenges in the life of a child with autism are difficulties in communication, behavior, and social interaction. Early diagnosis of this ne...
Objective: We developed and validated a detection-guided artifact removal framework for clinical electroencephalography (EEG). The framework applies a...
Brain Foundation Models (BFMs) are transforming neuroscience by enabling scalable and transferable learning from neural signals, advancing both clinic...
Automated EEG monitoring requires clinician-level precision for seizure detection and reporting. Clinical EEG recordings exceed LLM context windows, r...
Cognitive load, the mental effort required during working memory, is central to neuroscience, psychology, and human-computer interaction. Accurate ass...
Isolated REM sleep behavior disorder (iRBD) is a key prodromal marker of Parkinson's disease (PD), and video-polysomnography (vPSG) remains the diagno...
To be practical for real-life applications, models for brain-computer interfaces must be easily and quickly deployable on new subjects, effective on a...
Isolated REM sleep behavior disorder (iRBD) is a key prodromal marker of Parkinson's disease (PD), and video-polysomnography (vPSG) remains the diagno...
Early detection of dementia enables timely intervention and better care planning. Electroencephalography, being accessible and noninvasive, offers a p...
Accurate and timely seizure detection from Electroencephalography (EEG) is critical for clinical intervention, yet manual review of long-term recordin...