Seizures

Applying deep learning to single-trial EEG data provides evidence for complementary theories on action control.

Efficient action control is indispensable for goal-directed behaviour. Different theories have stres...

Latent Phase Identification of High-Frequency Micro-Scale Gamma Spike Transients in the Hypoxic Ischemic EEG of Preterm Fetal Sheep Using Spectral Analysis and Fuzzy Classifiers.

Premature babies are at high risk of serious neurodevelopmental disabilities, which in many cases ar...

SeizureBank: A Repository of Analysis-ready Seizure Signal Data.

Approximately 60 million people worldwide suffer from epileptic seizures. A key challenge in machine...

A Residual Based Attention Model for EEG Based Sleep Staging.

Sleep staging is to score the sleep state of a subject into different sleep stages such as Wake and ...

Automated classification of five seizure onset patterns from intracranial electroencephalogram signals.

OBJECTIVE: The electroencephalographic (EEG) signals contain information about seizures and their on...

Constructing large-scale cortical brain networks from scalp EEG with Bayesian nonnegative matrix factorization.

A large-scale network provides a high hierarchical level for understanding the adaptive adjustment o...

Novel Deep Learning Network Analysis of Electrical Stimulation Mapping-Driven Diffusion MRI Tractography to Improve Preoperative Evaluation of Pediatric Epilepsy.

OBJECTIVE: To investigate the clinical utility of deep convolutional neural network (DCNN) tract cla...

A-phase classification using convolutional neural networks.

A series of short events, called A-phases, can be observed in the human electroencephalogram (EEG) d...

Computer-assisted estimation of interictal discharge burden in idiopathic generalized epilepsy.

Investigating the temporal dynamics of electroencephalogram (EEG) microstates using recurrent neural networks.

Electroencephalogram (EEG) microstates that represent quasi-stable, global neuronal activity are con...

A Comprehensive Machine-Learning-Based Software Pipeline to Classify EEG Signals: A Case Study on PNES vs. Control Subjects.

The diagnosis of psychogenic nonepileptic seizures (PNES) by means of electroencephalography (EEG) i...

Multimodal data analysis of epileptic EEG and rs-fMRI via deep learning and edge computing.

BACKGROUND AND OBJECTIVE: Multimodal data analysis and large-scale computational capability is enter...

A Review of Algorithm & Hardware Design for AI-Based Biomedical Applications.

This paper reviews the state of the arts and trends of the AI-Based biomedical processing algorithms...

Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection.

Epilepsy is a chronic neurological disorder characterized by the occurrence of spontaneous seizures,...

QuPWM: Feature Extraction Method for Epileptic Spike Classification.

Epilepsy is a neurological disorder ranked as the second most serious neurological disease known to ...

Somatosensory evoked fields predict response to vagus nerve stimulation.

There is an unmet need to develop robust predictive algorithms to preoperatively identify pediatric ...

A deep CNN approach to decode motor preparation of upper limbs from time-frequency maps of EEG signals at source level.

A system that can detect the intention to move and decode the planned movement could help all those ...

A Sparse EEG-Informed fMRI Model for Hybrid EEG-fMRI Neurofeedback Prediction.

Measures of brain activity through functional magnetic resonance imaging (fMRI) or electroencephalog...

The feature extraction of resting-state EEG signal from amnestic mild cognitive impairment with type 2 diabetes mellitus based on feature-fusion multispectral image method.

Recently, combining feature extraction and classification method of electroencephalogram (EEG) signa...

EEG based multi-class seizure type classification using convolutional neural network and transfer learning.

Recognition of epileptic seizure type is essential for the neurosurgeon to understand the cortical c...

Directed EEG neural network analysis by LAPPS (p≤1) Penalized sparse Granger approach.

The conventional multivariate Granger Analysis (GA) of directed interactions has been widely applied...