Neurology

Seizures

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

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A hybrid CNN-Bi-LSTM model with feature fusion for accurate epilepsy seizure detection.

BACKGROUND: The diagnosis and treatment of epilepsy continue to face numerous challenges, highlighti...

EEG Signals Classification Related to Visual Objects Using Long Short-Term Memory Network and Nonlinear Interval Type-2 Fuzzy Regression.

By gaining insights into how brain activity is encoded and decoded, we enhance our understanding of ...

A hybrid network using transformer with modified locally linear embedding and sliding window convolution for EEG decoding.

. Brain-computer interface(BCI) is leveraged by artificial intelligence in EEG signal decoding, whic...

Cognitive load detection through EEG lead wise feature optimization and ensemble classification.

Cognitive load stimulates neural activity, essential for understanding the brain's response to stres...

DHCT-GAN: Improving EEG Signal Quality with a Dual-Branch Hybrid CNN-Transformer Network.

Electroencephalogram (EEG) signals are important bioelectrical signals widely used in brain activity...

Leveraging deep learning for robust EEG analysis in mental health monitoring.

INTRODUCTION: Mental health monitoring utilizing EEG analysis has garnered notable interest due to t...

Detection of focal cortical dysplasia: Development and multicentric evaluation of artificial intelligence models.

OBJECTIVE: Focal cortical dysplasia (FCD) is a common cause of drug-resistant focal epilepsy but can...

A Novel State Space Model with Dynamic Graphic Neural Network for EEG Event Detection.

Electroencephalography (EEG) is a widely used physiological signal to obtain information of brain ac...

EEG-based emotion recognition using multi-scale dynamic CNN and gated transformer.

Emotions play a crucial role in human thoughts, cognitive processes, and decision-making. EEG has be...

Decoding of pain during heel lancing in human neonates with EEG signal and machine learning approach.

Currently, pain assessment using electroencephalogram signals and machine learning methods in clinic...

Emotion recognition using multi-scale EEG features through graph convolutional attention network.

Emotion recognition via electroencephalogram (EEG) signals holds significant promise across various ...

A novel way to use cross-validation to measure connectivity by machine learning allows epilepsy surgery outcome prediction.

The rate of success of epilepsy surgery, ensuring seizure-freedom, is limited by the lack of epilept...

Preictal period optimization for deep learning-based epileptic seizure prediction.

. Accurate seizure prediction could prove critical for improving patient safety and quality of life ...

Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism.

Epileptic seizures, a prevalent neurological condition, necessitate precise and prompt identificatio...

Multi-modal cross-domain self-supervised pre-training for fMRI and EEG fusion.

Neuroimaging techniques including functional magnetic resonance imaging (fMRI) and electroencephalog...

Multimodal data-based human motion intention prediction using adaptive hybrid deep learning network for movement challenged person.

Recently, social demands for a good quality of life have increased among the elderly and disabled pe...

Exploring the Versatility of Spiking Neural Networks: Applications Across Diverse Scenarios.

In the last few decades, Artificial Neural Networks have become more and more important, evolving in...

Detection and location of EEG events using deep learning visual inspection.

The electroencephalogram (EEG) is a major diagnostic tool that provides detailed insight into the el...

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