Neurology

Seizures

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

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Schizophrenia recognition based on three-dimensional adaptive graph convolutional neural network.

Previous deep learning-based brain network research has made significant progress in understanding t...

Specific endophenotypes in EEG microstates for methamphetamine use disorder.

BACKGROUND: Electroencephalogram (EEG) microstates, which reflect large-scale resting-state networks...

Enhanced electroencephalogram signal classification: A hybrid convolutional neural network with attention-based feature selection.

Accurate recognition and classification of motor imagery electroencephalogram (MI-EEG) signals are c...

Multi-branch convolutional neural network with cross-attention mechanism for emotion recognition.

Research on emotion recognition is an interesting area because of its wide-ranging applications in e...

Unveiling encephalopathy signatures: A deep learning approach with locality-preserving features and hybrid neural network for EEG analysis.

EEG signals exhibit spatio-temporal characteristics due to the neural activity dispersion in space o...

Artificial intelligent based control strategy for reach and grasp of multi-objects using brain-controlled robotic arm system.

Brain-controlled robotic arm systems are designed to provide a method of communication and control f...

Graph convolution network-based eeg signal analysis: a review.

With the advancement of artificial intelligence technology, more and more effective methods are bein...

A temporal-spatial feature fusion network for emotion recognition with individual differences reduction.

PURPOSE: In the context of EEG-based emotion recognition tasks, a conventional strategy involves the...

Prediction of Pharmacoresistance in Drug-Naïve Temporal Lobe Epilepsy Using Ictal EEGs Based on Convolutional Neural Network.

Approximately 30%-40% of epilepsy patients do not respond well to adequate anti-seizure medications ...

Utilizing machine learning techniques for EEG assessment in the diagnosis of epileptic seizures in the brain: A systematic review and meta-analysis.

PURPOSE: Advancements in Machine Learning (ML) techniques have revolutionized diagnosing and monitor...

Beyond averaging: A transformer approach to decoding event related brain potentials.

The objective of this study is to assess the potential of a transformer-based deep learning approach...

Machine learning-based algorithm of drug-resistant prediction in newly diagnosed patients with temporal lobe epilepsy.

OBJECTIVES: To develop a predicted algorithm for drug-resistant epilepsy (DRE) in newly diagnosed te...

The 'Sandwich' meta-framework for architecture agnostic deep privacy-preserving transfer learning for non-invasive brainwave decoding.

. Machine learning has enhanced the performance of decoding signals indicating human behaviour. Elec...

Using artificial intelligence to optimize anti-seizure treatment and EEG-guided decisions in severe brain injury.

Electroencephalography (EEG) is invaluable in the management of acute neurological emergencies. Char...

Eeg Microstates and Balance Parameters for Stroke Discrimination: A Machine Learning Approach.

Electroencephalography microstates (EEG-MS) show promise to be a neurobiological biomarker in stroke...

Prediction Trough Concentrations of Valproic Acid Among Chinese Adult Patients with Epilepsy Using Machine Learning Techniques.

OBJECTIVE: This study aimed to establish an optimal model based on machine learning (ML) to predict ...

Automated karyogram analysis for early detection of genetic and neurodegenerative disorders: a hybrid machine learning approach.

Anomalous chromosomes are the cause of genetic diseases such as cancer, Alzheimer's, Parkinson's, ep...

[Application and considerations of artificial intelligence and neuroimaging in the study of brain effect mechanisms of acupuncture and moxibustion].

Electroencephalography (EEG) and magnetic resonance imaging (MRI), as neuroimaging technologies, pro...

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