Neurology

Seizures

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

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Fused CNN-LSTM deep learning emotion recognition model using electroencephalography signals.

The traditional machine learning-based emotion recognition models have shown effective performance ...

Generalized Deep Learning EEG Models for Cross-Participant and Cross-Task Detection of the Vigilance Decrement in Sustained Attention Tasks.

Tasks which require sustained attention over a lengthy period of time have been a focal point of cog...

A deep learning method for single-trial EEG classification in RSVP task based on spatiotemporal features of ERPs.

. Single-trial electroencephalography (EEG) classification is of great importance in the rapid seria...

Linear and non-linear feature extraction from rat electrocorticograms for seizure detection by support vector machine.

Seizures, the main symptom of epilepsy, are provoked due to a neurological disorder that underlies t...

A Hybrid-Domain Deep Learning-Based BCI For Discriminating Hand Motion Planning From EEG Sources.

In this paper, a hybrid-domain deep learning (DL)-based neural system is proposed to decode hand mov...

EEG Channel Correlation Based Model for Emotion Recognition.

Emotion recognition using Artificial Intelligence (AI) is a fundamental prerequisite to improve Huma...

Machine Learning for personalised stress detection: Inter-individual variability of EEG-ECG markers for acute-stress response.

Stress appears as a response for a broad variety of physiological stimuli. It does vary among indivi...

Detection of EEG burst-suppression in neurocritical care patients using an unsupervised machine learning algorithm.

OBJECTIVE: The burst suppression pattern in clinical electroencephalographic (EEG) recordings is an ...

Video-Based Detection of Generalized Tonic-Clonic Seizures Using Deep Learning.

Timely detection of seizures is crucial to implement optimal interventions, and may help reduce the ...

A deep learning based ensemble learning method for epileptic seizure prediction.

In epilepsy, patients suffer from seizures which cannot be controlled with medicines or surgical tre...

Affective Computing on Machine Learning-Based Emotion Recognition Using a Self-Made EEG Device.

In this research, we develop an affective computing method based on machine learning for emotion rec...

EEG-Based Emotion Recognition by Convolutional Neural Network with Multi-Scale Kernels.

Besides facial or gesture-based emotion recognition, Electroencephalogram (EEG) data have been drawi...

FLDNet: Frame-Level Distilling Neural Network for EEG Emotion Recognition.

Based on the current research on EEG emotion recognition, there are some limitations, such as hand-e...

Predicting subclinical psychotic-like experiences on a continuum using machine learning.

Previous studies applying machine learning methods to psychosis have primarily been concerned with t...

A deep learning framework with multi-perspective fusion for interictal epileptiform discharges detection in scalp electroencephalogram.

Interictal epileptiform discharges (IEDs) are an important and widely accepted biomarker used in the...

Automated Annotation of Epileptiform Burden and Its Association with Outcomes.

OBJECTIVE: This study was undertaken to determine the dose-response relation between epileptiform ac...

End-to-end learnable EEG channel selection for deep neural networks with Gumbel-softmax.

To develop an efficient, embedded electroencephalogram (EEG) channel selection approach for deep neu...

Deep Learning of Explainable EEG Patterns as Dynamic Spatiotemporal Clusters and Rules in a Brain-Inspired Spiking Neural Network.

The paper proposes a new method for deep learning and knowledge discovery in a brain-inspired Spikin...

Time-Frequency Decomposition of Scalp Electroencephalograms Improves Deep Learning-Based Epilepsy Diagnosis.

Epilepsy diagnosis based on Interictal Epileptiform Discharges (IEDs) in scalp electroencephalograms...

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