Neurology

Seizures

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

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SeizureFormer: A Transformer Model for IEA-Based Seizure Risk Forecasting

We present SeizureFormer, a Transformer-based model for long-term seizure risk forecasting using i...

An Empirical Investigation of Reconstruction-Based Models for Seizure Prediction from ECG Signals

Epileptic seizures are sudden neurological disorders characterized by abnormal, excessive neuronal...

A Systematic Literature Review of Unmanned Aerial Vehicles for Healthcare and Emergency Services

Unmanned aerial vehicles (UAVs), initially developed for military applications, are now used in va...

Brain Signatures of Time Perception in Virtual Reality

Achieving a high level of immersion and adaptation in virtual reality (VR) requires precise measur...

Focal Cortical Dysplasia Type II Detection Using Cross Modality Transfer Learning and Grad-CAM in 3D-CNNs for MRI Analysis

Focal cortical dysplasia (FCD) type II is a major cause of drug-resistant epilepsy, often curable ...

Classification of ADHD and Healthy Children Using EEG Based Multi-Band Spatial Features Enhancement

Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder in childre...

EEG2GAIT: A Hierarchical Graph Convolutional Network for EEG-based Gait Decoding

Decoding gait dynamics from EEG signals presents significant challenges due to the complex spatial...

Flexible and Explainable Graph Analysis for EEG-based Alzheimer's Disease Classification

Alzheimer's Disease is a progressive neurological disorder that is one of the most common forms of...

SeizureTransformer: Scaling U-Net with Transformer for Simultaneous Time-Step Level Seizure Detection from Long EEG Recordings

Epilepsy is a common neurological disorder that affects around 65 million people worldwide. Detect...

Towards Practical Emotion Recognition: An Unsupervised Source-Free Approach for EEG Domain Adaptation

Emotion recognition is crucial for advancing mental health, healthcare, and technologies like brai...

Automated Video-EEG Analysis in Epilepsy Studies: Advances and Challenges

Epilepsy is typically diagnosed through electroencephalography (EEG) and long-term video-EEG (vEEG...

A Systematic Review of EEG-based Machine Intelligence Algorithms for Depression Diagnosis, and Monitoring

Depression disorder is a serious health condition that has affected the lives of millions of peopl...

FACE: Few-shot Adapter with Cross-view Fusion for Cross-subject EEG Emotion Recognition

Cross-subject EEG emotion recognition is challenged by significant inter-subject variability and i...

Exploring Deep Learning Models for EEG Neural Decoding

Neural decoding is an important method in cognitive neuroscience that aims to decode brain represe...

EEG-CLIP : Learning EEG representations from natural language descriptions

Deep networks for electroencephalogram (EEG) decoding are currently often trained to only solve a ...

Enhanced EEG Forecasting: A Probabilistic Deep Learning Approach.

Forecasting electroencephalography (EEG) signals, that is, estimating future values of the time seri...

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Combined impact of grey and superficial white matter abnormalities: implications for epilepsy surgery

Drug-resistant focal epilepsy is associated with abnormalities in the brain in both grey matter (G...

Oscillatory Signatures of Parkinson's Disease: Central and Parietal EEG Alterations Across Multiple Frequency Bands

This study investigates EEG as a potential early biomarker by applying deep learning techniques to...

When neural implant meets multimodal LLM: A dual-loop system for neuromodulation and naturalistic neuralbehavioral research

We propose a novel dual-loop system that synergistically combines responsive neurostimulation (RNS...

BioSerenity-E1: a self-supervised EEG model for medical applications

Electroencephalography (EEG) serves as an essential diagnostic tool in neurology; however, its acc...

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