Seizures

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 cognitive fatigue research for decades, with these tasks including air traffic control, watchkeeping, baggage inspection, and many others. Recent research into physiological markers of mental fatigue indicate that markers exist which extend across all individuals and all types of vigilance tasks. This ...

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 serial visual presentation (RSVP) task. Convolutional neural networks (CNNs), as one of the mainstream deep learning methods, have been proven to be effective in extracting RSVP EEG features. However, most existing CNN models for EEG classification do not consider the phase-locked characteristic of event...

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 the disease. The accurate detection of seizures is ...

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 movement preparation phases from electroencephalograp...

EEG Channel Correlation Based Model for Emotion Recognition.

Emotion recognition using Artificial Intelligence (AI) is a fundamental prerequisite to improve Human-Computer Interaction (HCI). Recognizing emotion ...

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 individuals in amplitude, phase and frequency. Thus, the...

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 important diagnostic tool because of its associati...

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 risk of sudden unexpected death in epilepsy (SUDEP...

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 treatments in more than 30% of the cases. Prediction ...

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 recognition using a wireless protocol and a wearable ...

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

Besides facial or gesture-based emotion recognition, Electroencephalogram (EEG) data have been drawing attention thanks to their capability in counter...

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-engineered features, redundant and meaningless sign...

Graph-theory based degree centrality combined with machine learning algorithms can predict response to treatment with antiepileptic medications in children with epilepsy.

BACKGROUND AND PURPOSE: The purpose of the current study is to detect changes of graph-theory-based degree centrality (DC) and their relationship with...

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

Previous studies applying machine learning methods to psychosis have primarily been concerned with the binary classification of chronic schizophrenia ...

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 diagnosis of epilepsy based on scalp electroencep...

Automated Annotation of Epileptiform Burden and Its Association with Outcomes.

OBJECTIVE: This study was undertaken to determine the dose-response relation between epileptiform activity burden and outcomes in acutely ill patients...

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 neural networks, allowing us to match the channel sel...

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 Spiking Neural Networks (SNN) architecture that enhances...

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

Epilepsy diagnosis based on Interictal Epileptiform Discharges (IEDs) in scalp electroencephalograms (EEGs) is laborious and often subjective. Therefo...

Emotion-Driven Analysis and Control of Human-Robot Interactions in Collaborative Applications.

The utilization of robotic systems has been increasing in the last decade. This increase has been derived by the evolvement in the computational capab...