Seizures

EEG decoding with spatiotemporal convolutional neural network for visualization and closed-loop control of sensorimotor activities: A simultaneous EEG-fMRI study.

Closed-loop neurofeedback training utilizes neural signals such as scalp electroencephalograms (EEG) to manipulate specific neural activities and the associated behavioral performance. A spatiotemporal filter for high-density whole-head scalp EEG using a convolutional neural network can overcome the ambiguity of the signaling source because each EEG signal includes information on the remote region...

EEG-ImageNet: An Electroencephalogram Dataset and Benchmarks with Image Visual Stimuli of Multi-Granularity Labels

Identifying and reconstructing what we see from brain activity gives us a special insight into investigating how the biological visual system represents the world. While recent efforts have achieved high-performance image classification and high-quality image reconstruction from brain signals collected by Functional Magnetic Resonance Imaging (fMRI) or magnetoencephalogram (MEG), the expensivene...

Mind's Eye: Image Recognition by EEG via Multimodal Similarity-Keeping Contrastive Learning

Decoding images from non-invasive electroencephalographic (EEG) signals has been a grand challenge in understanding how the human brain process visu...

EEG-based machine learning models for the prediction of phenoconversion time and subtype in isolated rapid eye movement sleep behavior disorder.

STUDY OBJECTIVES: Isolated rapid eye movement sleep behavior disorder (iRBD) is a prodromal stage of α-synucleinopathies and eventually phenoconverts ...

Improving epilepsy diagnosis across the lifespan: approaches and innovations.

Epilepsy diagnosis is often delayed or inaccurate, exposing people to ongoing seizures and their substantial consequences until effective treatment is...

[Automatic epilepsy detection with an attention-based multiscale residual network].

The deep learning-based automatic detection of epilepsy electroencephalogram (EEG), which can avoid the artificial influence, has attracted much atten...

Improving Diagnostic Performance of MRI for Temporal Lobe Epilepsy With Deep Learning-Based Image Reconstruction in Patients With Suspected Focal Epilepsy.

OBJECTIVE: To evaluate the diagnostic performance and image quality of 1.5-mm slice thickness MRI with deep learning-based image reconstruction (1.5-m...

Human Visual Cortex and Deep Convolutional Neural Network Care Deeply about Object Background.

Deep convolutional neural networks (DCNNs) are able to partially predict brain activity during object categorization tasks, but factors contributing t...

Cross-subject EEG-based emotion recognition through dynamic optimization of random forest with sparrow search algorithm.

The objective of EEG-based emotion recognition is to classify emotions by decoding signals, with potential applications in the fields of artificial in...

Optimal Graph Representations and Neural Networks for Seizure Detection Using Intracranial EEG Data

In recent years, several machine-learning (ML) solutions have been proposed to solve the problems of seizure detection, seizure characterization, seiz...

Decoding pre-movement neural activity from thalamic LFPs for adaptive neurostimulation in tremor patients

Understanding the neural mechanisms underlying movement initiation is crucial for advancing movement-driven adaptive deep brain stimulation therapies ...

Neural Mosaics: Detecting Aberrant Brain Interactions using Algebraic Topology and Generative Artificial Intelligence.

Epilepsy affects over 50 million persons worldwide, with less than 50% achieving long-term success following surgery. Traditional electrophysiology si...

[Safety of robot-assisted implantation of deep electrodes for invasive stereo-EEG monitoring].

UNLABELLED: Robot-assisted implantation of deep electrodes for stereo-EEG monitoring has become popular in recent years in patients with drug-resistan...

ADT Network: A Novel Nonlinear Method for Decoding Speech Envelopes From EEG Signals.

Decoding speech envelopes from electroencephalogram (EEG) signals holds potential as a research tool for objectively assessing auditory processing, wh...

BiTCAN: A emotion recognition network based on saliency in brain cognition.

In recent years, with the continuous development of artificial intelligence and brain-computer interfaces, emotion recognition based on electroencepha...

Predicting Antiseizure Medication Treatment in Children with Rare Tuberous Sclerosis Complex-Related Epilepsy Using Deep Learning.

BACKGROUND AND PURPOSE: Tuberous sclerosis complex disease is a rare, multisystem genetic disease, but appropriate drug treatment allows many pediatri...

AGL-Net: An Efficient Neural Network for EEG-Based Driver Fatigue Detection.

BACKGROUND: In recent years, road traffic safety has become a prominent issue due to the worldwide proliferation of vehicles on roads. The challenge o...

A regression model combined convolutional neural network and recurrent neural network for electroencephalogram-based cross-subject fatigue detection.

Fatigue, one of the most important factors affecting road safety, has attracted many researchers' attention. Most existing fatigue detection methods a...

A convolutional neural network-based decision support system for neonatal quiet sleep detection.

Sleep plays an important role in neonatal brain and physical development, making its detection and characterization important for assessing early-stag...

Automated Interpretation of Clinical Electroencephalograms Using Artificial Intelligence.

IMPORTANCE: Electroencephalograms (EEGs) are a fundamental evaluation in neurology but require special expertise unavailable in many regions of the wo...