Neurology

Seizures

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

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FEMBA: Efficient and Scalable EEG Analysis with a Bidirectional Mamba Foundation Model

Accurate and efficient electroencephalography (EEG) analysis is essential for detecting seizures a...

Neurophysiological correlates to the human brain complexity through $q$-statistical analysis of electroencephalogram

The prospects of assessing neural complexity (NC) by $q$-statistics of the systemic organization o...

Protecting Intellectual Property of EEG-based Neural Networks with Watermarking

EEG-based neural networks, pivotal in medical diagnosis and brain-computer interfaces, face signif...

From Bedside to Desktop: A Data Protocol for Normative Intracranial EEG and Abnormality Mapping

Normative mapping is a framework used to map population-level features of health-related variables...

Fine-Tuning Strategies for Continual Online EEG Motor Imagery Decoding: Insights from a Large-Scale Longitudinal Study

This study investigates continual fine-tuning strategies for deep learning in online longitudinal ...

Human fields and their impact on brain waves A pilot study

During brain function, groups of neurons fire synchronously. When these groups are large enough, t...

Spatio-Temporal Progressive Attention Model for EEG Classification in Rapid Serial Visual Presentation Task

As a type of multi-dimensional sequential data, the spatial and temporal dependencies of electroen...

Milmer: a Framework for Multiple Instance Learning based Multimodal Emotion Recognition

Emotions play a crucial role in human behavior and decision-making, making emotion recognition a k...

SSRepL-ADHD: Adaptive Complex Representation Learning Framework for ADHD Detection from Visual Attention Tasks

Self Supervised Representation Learning (SSRepL) can capture meaningful and robust representations...

Vagus nerve stimulation as a modulator of feedforward and feedback neural transmission

Vagus nerve stimulation (VNS) has emerged as a promising therapeutic intervention across various n...

Machine Learning Fairness for Depression Detection using EEG Data

This paper presents the very first attempt to evaluate machine learning fairness for depression de...

On the challenges of detecting MCI using EEG in the wild

Recent studies have shown promising results in the detection of Mild Cognitive Impairment (MCI) us...

EEG-ReMinD: Enhancing Neurodegenerative EEG Decoding through Self-Supervised State Reconstruction-Primed Riemannian Dynamics

The development of EEG decoding algorithms confronts challenges such as data sparsity, subject var...

Exploring the distribution of connectivity weights in resting-state EEG networks

The resting-state brain networks (RSNs) reflects the functional connectivity patterns between brai...

Perception-Guided EEG Analysis: A Deep Learning Approach Inspired by Level of Detail (LOD) Theory

Objective: This study explores a novel deep learning approach for EEG analysis and perceptual stat...

Motif Discovery Framework for Psychiatric EEG Data Classification

In current medical practice, patients undergoing depression treatment must wait four to six weeks ...

Exploring EEG and Eye Movement Fusion for Multi-Class Target RSVP-BCI

Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interfaces (BCIs) facilitate high-thr...

Integrating Language-Image Prior into EEG Decoding for Cross-Task Zero-Calibration RSVP-BCI

Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interface (BCI) is an effective techn...

Automated Detection of Epileptic Spikes and Seizures Incorporating a Novel Spatial Clustering Prior

A Magnetoencephalography (MEG) time-series recording consists of multi-channel signals collected b...

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