Seizures

Evaluating and Validating an Artificial Intelligence Model for Automated Electroencephalogram Analysis: Implications for Clinical Practice

Epilepsy affects around 50 million people worldwide and remains a major diagnostic challenge, particularly in resource-limited settings. Electroencephalography (EEG) is essential for diagnosis but relies heavily on expert interpretation, often limited by workforce shortages. Artificial intelligence (AI) offers a promising solution to automate EEG interpretation, enhance diagnostic accuracy, and im...

DeepCRI: Real-time EEG-based Prognostication after Cardiac Arrest

Accurate prediction of neurological outcome after cardiac arrest is essential for guiding intensive care decisions. Electroencephalography (EEG) supports prognostication; however, interpretation relies on expert judgment and is often subjective and delayed. We developed DeepCRI, a bedside-integrated deep learning system that produces continuously updated prognostic trajectories during the first 36...

A Comparison of Two Deep Learning Approaches to Distinguish Functional Dissociative from Epileptic Seizures Using Event Videos

Differentiating between motor functional dissociative seizures (FDS) and motor epileptic seizures (ES) is a common diagnostic challenge, requiring vid...

Brain natural frequencies as physiologically meaningful biomarkers for machine-learning detection of Parkinson’s disease

In this study, we investigated whether individual brain maps of natural frequencies derived from EEG can serve as physiologically meaningful biomarker...

Multiscale-Multistage Temporal Convolutional Network for EEG-Based Mild Cognitive Impairment Detection

Mild cognitive impairment (MCI) is an intermediate stage between normal ageing and dementia, with affected individuals at a higher risk of progressing...

Is it possible to vaccinate AI against bias? An exploratory study in epilepsy

Large language models are increasingly used for clinical decision support yet may perpetuate socioeconomic biases. Whether simple prompt-based interve...

Surface EEG to identify cognitive motor dissociation after acute brain injury

Cognitive motor dissociation (CMD) is associated with long-term recovery in acute brain injury, but CMD testing is only available in few centers. Our ...

Identifying EEG biomarkers of sense of embodiment in virtual reality: insights from spatio-spectral features.

The Sense of Embodiment (SoE) refers to the subjective experience of perceiving a non-biological body part as one's own. Virtual Reality (VR) provides...

The changes in brain network functional gradients and dynamic functional connectivity in SeLECTS patients revealing disruptive and compensatory mechanisms in brain networks.

BACKGROUND: Self-limited epilepsy with centrotemporal spikes (SeLECTS), a common childhood focal epilepsy syndrome, is linked to cognitive impairments...

Research on emotion recognition using sparse EEG channels and cross-subject modeling based on CNN-KAN-[Formula: see text] model.

Emotion recognition plays a significant role in artificial intelligence and human-computer interaction. Electroencephalography (EEG) signals, due to t...

Smart Seizure Detection System: Machine Learning Based Model in Healthcare IoT.

BACKGROUND: Epilepsy, the tendency to have recurrent seizures, can have various causes, including brain tumors, genetics, stroke, brain injury, infect...

ML-based Models as a Strategy to Discover Novel Antiepileptic Drugs Targeting Sodium Receptor Channel.

BACKGROUND: Epilepsy remains the most common and chronic disorder demanding longterm management. The impact of epilepsy disease is a cause of great co...

Intelligent Control to Suppress Epileptic Seizures in the Amygdala: In Silico Investigation Using a Network of Izhikevich Neurons.

Closed-loop electricalstimulation of brain structures is one of the most promising techniques to suppress epileptic seizures in drug-resistant refract...

A Systematic Review of Machine Learning Methods for Multimodal EEG Data in Clinical Application

Machine learning (ML) and deep learning (DL) techniques have been widely applied to analyze electroencephalography (EEG) signals for disease diagnos...

Improving SSVEP BCI Spellers With Data Augmentation and Language Models

Steady-State Visual Evoked Potential (SSVEP) spellers are a promising communication tool for individuals with disabilities. This Brain-Computer Inte...

Comprehensive Review of EEG-to-Output Research: Decoding Neural Signals into Images, Videos, and Audio

Electroencephalography (EEG) is an invaluable tool in neuroscience, offering insights into brain activity with high temporal resolution. Recent adva...

Revealing the Self: Brainwave-Based Human Trait Identification

People exhibit unique emotional responses. In the same scenario, the emotional reactions of two individuals can be either similar or vastly differen...

[Three-dimensional convolutional neural network based on spatial-spectral feature pictures learning for decoding motor imagery electroencephalography signal].

The brain-computer interface (BCI) based on motor imagery electroencephalography (EEG) shows great potential in neurorehabilitation due to its non-inv...

RNN-Based Models for Predicting Seizure Onset in Epileptic Patients

Early management and better clinical outcomes for epileptic patients depend on seizure prediction. The accuracy and false alarm rates of existing sy...

Utilizing Causal Network Markers to Identify Tipping Points ahead of Critical Transition

Early-warning signals of delicate design are always used to predict critical transitions in complex systems, which makes it possible to render the s...