Seizures

Computational modeling of the effects of EEG volume conduction on functional connectivity metrics. Application to Alzheimer's disease continuum.

OBJECTIVE: The aim of this study was to evaluate the effect of electroencephalographic (EEG) volume conduction in different measures of functional connectivity and to characterize the EEG coupling alterations at the different stages of dementia due to Alzheimer's disease (AD).

Incorporating feature selection methods into a machine learning-based neonatal seizure diagnosis.

The present study developed a feature selection (FS)-based decision support system using the electroencephalography (EEG) signals recorded from neonates with and without seizures. The study employed 10 different FS algorithms to reduce the classification cost by using fewer features and to improve the classification performance of the model by removing the irrelevant features. In doing so, the cla...

Convolutional neural networks for decoding of covert attention focus and saliency maps for EEG feature visualization.

OBJECTIVE: Convolutional neural networks (CNNs) have proven successful as function approximators and have therefore been used for classification probl...

An Exploration of Machine Learning Methods for Robust Boredom Classification Using EEG and GSR Data.

In recent years, affective computing has been actively researched to provide a higher level of emotion-awareness. Numerous studies have been conducted...

Spiking Neural Networks applied to the classification of motor tasks in EEG signals.

Motivated by the recent progress of Spiking Neural Network (SNN) models in pattern recognition, we report on the development and evaluation of brain s...

Optimal selection of SOP and SPH using fuzzy inference system for on-line epileptic seizure prediction based on EEG phase synchronization.

Living conditions of patients with refractory epilepsy will be significantly improved by a successful prediction of epileptic seizures. A proper warni...

Automatic Seizure Detection Based on S-Transform and Deep Convolutional Neural Network.

Automatic seizure detection is significant for the diagnosis of epilepsy and reducing the massive workload of reviewing continuous EEGs. In this work,...

A strategy combining intrinsic time-scale decomposition and a feedforward neural network for automatic seizure detection.

UNLABELLED: Epilepsy is a common neurological disorder which can occur in people of all ages globally. For the clinical treatment of epileptic patient...

Orthogonal convolutional neural networks for automatic sleep stage classification based on single-channel EEG.

BACKGROUND AND OBJECTIVE: In recent years, several automatic sleep stage classification methods based on convolutional neural networks (CNN) by learni...

Seizure Prediction in Scalp EEG Using 3D Convolutional Neural Networks With an Image-Based Approach.

Epileptic seizures occur as a result of a process that develops over time and space in epileptic networks. In this study, we aim at developing a gener...

EEG-based single-channel authentication systems with optimum electrode placement for different mental activities.

BACKGROUND: Electroencephalogram (EEG) signals of a brain contain a unique pattern for each person and the potential for biometric applications. Authe...

A zero-shot learning approach to the development of brain-computer interfaces for image retrieval.

Brain decoding-the process of inferring a person's momentary cognitive state from their brain activity-has enormous potential in the field of human-co...

Deep Multi-View Feature Learning for EEG-Based Epileptic Seizure Detection.

Epilepsy is a neurological illness caused by abnormal discharge of brain neurons, where epileptic seizure can lead to life-threatening emergencies. By...

Recognition of words from brain-generated signals of speech-impaired people: Application of autoencoders as a neural Turing machine controller in deep neural networks.

There is an essential requirement to support people with speech and communication disabilities. A brain-computer interface using electroencephalograph...

A LightGBM-Based EEG Analysis Method for Driver Mental States Classification.

Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography- (EEG-) b...

Automatic detection of epileptic seizure based on approximate entropy, recurrence quantification analysis and convolutional neural networks.

Epilepsy is the most common neurological disorder in humans. Electroencephalogram is a prevalent tool for diagnosing the epileptic seizure activity in...

Nonconvulsive epileptic seizure monitoring with incremental learning.

Nonconvulsive epileptic seizures (NCSz) and nonconvulsive status epilepticus (NCSE) are two neurological entities associated with increment in morbidi...

World's fastest brain-computer interface: Combining EEG2Code with deep learning.

We present a novel approach based on deep learning for decoding sensory information from non-invasively recorded Electroencephalograms (EEG). It can e...

Bursts with High and Low Load of Epileptiform Spikes Show Context-Dependent Correlations in Epileptic Mice.

Hypersynchronous network activity is the defining hallmark of epilepsy and manifests in a wide spectrum of phenomena, of which electrographic activity...

Machine learning applications in epilepsy.

Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation o...