Neurology

Seizures

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

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Machine learning of brain-specific biomarkers from EEG.

BACKGROUND: Electroencephalography (EEG) has a long history as a clinical tool to study brain functi...

Effective Emotion Recognition by Learning Discriminative Graph Topologies in EEG Brain Networks.

Multichannel electroencephalogram (EEG) is an array signal that represents brain neural networks and...

A data augmentation procedure to improve detection of spike ripples in brain voltage recordings.

Epilepsy is a major neurological disorder characterized by recurrent, spontaneous seizures. For pati...

From data to decisions: AI and functional connectivity for diagnosis, prognosis, and recovery prediction in stroke.

Stroke is a severe medical condition which may lead to permanent disability conditions. The initial ...

A novel universal deep learning approach for accurate detection of epilepsy.

Epilepsy claims the lives of many people, so researchers strive to build highly accurate diagnostic ...

Toward calibration-free motor imagery brain-computer interfaces: a VGG-based convolutional neural network and WGAN approach.

Motor imagery (MI) represents one major paradigm of Brain-computer interfaces (BCIs) in which users ...

Multitask learning of a biophysically-detailed neuron model.

The human brain operates at multiple levels, from molecules to circuits, and understanding these com...

SQI-DOANet: electroencephalogram-based deep neural network for estimating signal quality index and depth of anaesthesia.

. Monitoring the depth of anaesthesia (DOA) during surgery is of critical importance. However, durin...

DGSD: Dynamical graph self-distillation for EEG-based auditory spatial attention detection.

Auditory Attention Detection (AAD) aims to detect the target speaker from brain signals in a multi-s...

Detection of Unfocused EEG Epochs by the Application of Machine Learning Algorithm.

Electroencephalography (EEG) is a non-invasive method used to track human brain activity over time. ...

3D printed dosage forms, where are we headed?

INTRODUCTION: 3D Printing (3DP) is an innovative fabrication technology that has gained enormous pop...

Study on the classification of sleep stages in EEG signals based on DoubleLinkSleepCLNet.

PURPOSE: The classification of sleep stages based on Electroencephalogram (EEG) changes has signific...

Adaptive node feature extraction in graph-based neural networks for brain diseases diagnosis using self-supervised learning.

Electroencephalography (EEG) has demonstrated significant value in diagnosing brain diseases. In par...

How the Degree of Anthropomorphism of Human-like Robots Affects Users' Perceptual and Emotional Processing: Evidence from an EEG Study.

Anthropomorphized robots are increasingly integrated into human social life, playing vital roles acr...

Insights from EEG analysis of evoked memory recalls using deep learning for emotion charting.

Affect recognition in a real-world, less constrained environment is the principal prerequisite of th...

A scheme combining feature fusion and hybrid deep learning models for epileptic seizure detection and prediction.

Epilepsy is one of the most well-known neurological disorders globally, leading to individuals exper...

Classification of subtask types and skill levels in robot-assisted surgery using EEG, eye-tracking, and machine learning.

BACKGROUND: Objective and standardized evaluation of surgical skills in robot-assisted surgery (RAS)...

Decoding lower-limb kinematic parameters during pedaling tasks using deep learning approaches and EEG.

Stroke is a neurological condition that usually results in the loss of voluntary control of body mov...

Differentiating loss of consciousness causes through artificial intelligence-enabled decoding of functional connectivity.

Differential diagnosis of acute loss of consciousness (LOC) is crucial due to the need for different...

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