Neurology

Seizures

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

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A single-joint multi-task motor imagery EEG signal recognition method based on Empirical Wavelet and Multi-Kernel Extreme Learning Machine.

BACKGROUND: In the pursuit of finer Brain-Computer Interface commands, research focus has shifted to...

Imagined speech classification exploiting EEG power spectrum features.

Imagined speech recognition has developed as a significant topic of research in the field of brain-c...

Artificial intelligence/machine learning for epilepsy and seizure diagnosis.

Accurate seizure and epilepsy diagnosis remains a challenging task due to the complexity and variabi...

Attention-based deep convolutional neural network for classification of generalized and focal epileptic seizures.

Epilepsy affects over 50 million people globally. Electroencephalography is critical for epilepsy di...

Convolutional spiking neural networks for intent detection based on anticipatory brain potentials using electroencephalogram.

Spiking neural networks (SNNs) are receiving increased attention because they mimic synaptic connect...

MSLTE: multiple self-supervised learning tasks for enhancing EEG emotion recognition.

. The instability of the EEG acquisition devices may lead to information loss in the channels or fre...

Differentiating ischemic stroke patients from healthy subjects using a large-scale, retrospective EEG database and machine learning methods.

OBJECTIVES: We set out to develop a machine learning model capable of distinguishing patients presen...

Robust compression and detection of epileptiform patterns in ECoG using a real-time spiking neural network hardware framework.

Interictal Epileptiform Discharges (IED) and High Frequency Oscillations (HFO) in intraoperative ele...

Editorial for the Special Issue "Sensing Brain Activity Using EEG and Machine Learning".

Sensing brain activity to reveal, analyze and recognize brain activity patterns has become a topic o...

vEpiNet: A multimodal interictal epileptiform discharge detection method based on video and electroencephalogram data.

To enhance deep learning-based automated interictal epileptiform discharge (IED) detection, this stu...

Explainable Deep-Learning Prediction for Brain-Computer Interfaces Supported Lower Extremity Motor Gains Based on Multistate Fusion.

Predicting the potential for recovery of motor function in stroke patients who undergo specific reha...

Enhancing cross-subject EEG emotion recognition through multi-source manifold metric transfer learning.

Transfer learning (TL) has demonstrated its efficacy in addressing the cross-subject domain adaptati...

An efficient Parkinson's disease detection framework: Leveraging time-frequency representation and AlexNet convolutional neural network.

Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting the quality of life o...

Harnessing machine learning for EEG signal analysis: Innovations in depth of anaesthesia assessment.

Anaesthesia, crucial to surgical practice, is undergoing renewed scrutiny due to the integration of ...

Manifold Learning-Based Common Spatial Pattern for EEG Signal Classification.

EEG signal classification using Riemannian manifolds has shown great potential. However, the huge co...

Data-driven normative values based on generative manifold learning for quantitative MRI.

In medicine, abnormalities in quantitative metrics such as the volume reduction of one brain region ...

Predicting an EEG-Based hypnotic time estimation with non-linear kernels of support vector machine algorithm.

Our ability to measure time is vital for daily life, technology use, and even mental health; however...

A machine learning based depression screening framework using temporal domain features of the electroencephalography signals.

Depression is a serious mental health disorder affecting millions of individuals worldwide. Timely a...

Applying Common Spatial Pattern and Convolutional Neural Network to Classify Movements via EEG Signals.

Developing an electroencephalography (EEG)-based brain-computer interface (BCI) system is crucial to...

Deep learning approaches for seizure video analysis: A review.

Seizure events can manifest as transient disruptions in the control of movements which may be organi...

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