Neurology

Seizures

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

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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. Ho...

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...

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...

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 recogn...

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 observation...

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...

LGFormer: integrating local and global representations for EEG decoding.

Electroencephalography (EEG) decoding is challenging because of its temporal variability and low sig...

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 neur...

Updates in Neonatal Seizures.

Neonatal seizures are a common medical emergency, necessitating prompt treatment. The most common et...

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 ...

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 c...

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...

Artificial intelligence applied to electroencephalography in epilepsy.

Artificial intelligence (AI) is progressively transforming all fields of medicine, promising substan...

Flexible Patched Brain Transformer model for EEG decoding.

Decoding the human brain using non-invasive methods is a significant challenge. This study aims to e...

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 ad...

Electroencephalography Decoding with Conditional Identification Generator.

Decoding Electroencephalography (EEG) signals are extremely useful for advancing and understanding h...

Recurrent and convolutional neural networks in classification of EEG signal for guided imagery and mental workload detection.

The Guided Imagery technique is reported to be used by therapists all over the world in order to inc...

Select for better learning: identifying high-quality training data for a multimodal cyclic transformer.

. Tonic-clonic seizures (TCSs), which present a significant risk for sudden unexpected death in epil...

Cognitive load assessment through EEG: A dataset from arithmetic and Stroop tasks.

This study introduces a thoughtfully curated dataset comprising electroencephalogram (EEG) recording...

EEG detection and recognition model for epilepsy based on dual attention mechanism.

In the field of clinical neurology, automated detection of epileptic seizures based on electroenceph...

Detection of freely moving thoughts using SVM and EEG signals.

Freely moving thought is a type of thinking that shifts from one topic to another without any overar...

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