Seizures

A diagnosis and prediction algorithm for juvenile myoclonic epilepsy based on clinical and quantitative EEG features.

OBJECTIVE: To develop an objective ensemble machine learning model combining clinical features and quantitative EEG metrics (phase locking value [PLV] and multiscale sample entropy [MSE]) to support accurate diagnosis of juvenile myoclonic epilepsy (JME).

Machine learning-based differentiation of schizophrenia and bipolar disorder using multiscale fuzzy entropy and relative power from resting-state EEG.

Schizophrenia (SZ) and bipolar disorder (BD) pose diagnostic challenges due to overlapping clinical symptoms and genetic factors, often resulting in misdiagnosis and suboptimal treatment outcomes. This study aimed to identify EEG-based biomarkers that can differentiate SZ from BD using multiscale fuzzy entropy (MFE) and relative power (RP) analyses and to evaluate their diagnostic utility using ma...

Artificial intelligence for the detection of interictal epileptiform discharges in EEG signals.

INTRODUCTION: Over the past decades, the integration of modern technologies - such as electronic health records, cloud computing, and artificial intel...

Exploring cortical excitability in children with cerebral palsy through lower limb robot training based on MI-BCI.

This study aims to compare brain activity differences under the motor imagery-brain-computer interface (MI-BCI), motor imagery (MI), and resting (REST...

DP-MP: a novel cross-subject fatigue detection framework with DANN-based prototypical representation and mix-up pairwise learning.

. Electroencephalography (EEG) is widely recognized as an effective method for detecting fatigue. However, practical applications of EEG for fatigue d...

An Efficient Graph Learning System for Emotion Recognition Inspired by the Cognitive Prior Graph of EEG Brain Network.

Benefiting from the high-temporal resolution of electroencephalogram (EEG), EEG-based emotion recognition has become one of the hotspots of affective ...

DC-ASTGCN: EEG Emotion Recognition Based on Fusion Deep Convolutional and Adaptive Spatio-Temporal Graph Convolutional Networks.

Thanks to advancements in artificial intelligence and brain-computer interface (BCI) research, there has been increasing attention towards emotion rec...

A Hybrid Artificial Intelligence System for Automated EEG Background Analysis and Report Generation.

Electroencephalography (EEG) plays a crucial role in the diagnosis of various neurological disorders. However, small hospitals and clinics often lack ...

LGFormer: integrating local and global representations for EEG decoding.

Electroencephalography (EEG) decoding is challenging because of its temporal variability and low signal-to-noise ratio, which complicate the extractio...

Explainable machine learning model based on EEG, ECG, and clinical features for predicting neurological outcomes in cardiac arrest patient.

Early and accurate prediction of neurological outcomes in comatose patients following cardiac arrest is critical for informed clinical decision-making...

An enhanced CNN-Bi-transformer based framework for detection of neurological illnesses through neurocardiac data fusion.

Classical approaches to diagnosis frequently rely on self-reported symptoms or clinician observations, which can make it difficult to examine mental h...

Hardware Optimization and Implementation of a 16-Channel Neural Tree Classifier for On-Chip Closed-Loop Neuromodulation.

This work presents the development of on-chip machine learning (ML) classifiers for implantable neuromodulation system-on-chips (SoCs), aimed at detec...

Updates in Neonatal Seizures.

Neonatal seizures are a common medical emergency, necessitating prompt treatment. The most common etiologies include hypoxic-ischemic encephalopathy, ...

Artificial intelligence applied to epilepsy imaging: Current status and future perspectives.

In recent years, artificial intelligence (AI) has become an increasingly prominent focus of medical research, significantly impacting epileptology as ...

Shared autonomy between human electroencephalography and TD3 deep reinforcement learning: A multi-agent copilot approach.

Deep reinforcement learning (RL) algorithms enable the development of fully autonomous agents that can interact with the environment. Brain-computer i...

Efficient Seizure Detection by Complementary Integration of Convolutional Neural Network and Vision Transformer.

Epilepsy, as a prevalent neurological disorder, is characterized by its high incidence, sudden onset, and recurrent nature. The development of an accu...

Artificial intelligence applied to electroencephalography in epilepsy.

Artificial intelligence (AI) is progressively transforming all fields of medicine, promising substantial changes in clinical practice. In the context ...

Enhancing convolutional neural networks in electroencephalogram driver drowsiness detection using human inspired optimizers.

Driver drowsiness is a significant safety concern, contributing to numerous traffic accidents. To address this issue, researchers have explored electr...

Flexible Patched Brain Transformer model for EEG decoding.

Decoding the human brain using non-invasive methods is a significant challenge. This study aims to enhance electroencephalography (EEG) decoding by de...

Electroencephalography Decoding with Conditional Identification Generator.

Decoding Electroencephalography (EEG) signals are extremely useful for advancing and understanding human-artificial intelligence (AI) interaction syst...