Seizures

Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram.

Seizure prediction has attracted growing attention as one of the most challenging predictive data analysis efforts to improve the life of patients with drug-resistant epilepsy and tonic seizures. Many outstanding studies have reported great results in providing sensible indirect (warning systems) or direct (interactive neural stimulation) control over refractory seizures, some of which achieved hi...

A fresh look at functional link neural network for motor imagery-based brain-computer interface.

BACKGROUND: Artificial neural networks (ANNs) are one of the widely used classifiers in the brain-computer interface (BCI) systems-based on noninvasive electroencephalography (EEG) signals. Among the different ANN architectures, the most commonly applied for BCI classifiers is the multilayer perceptron (MLP). When appropriately designed with optimal number of neuron layers and number of neurons pe...

Automated EEG-based screening of depression using deep convolutional neural network.

In recent years, advanced neurocomputing and machine learning techniques have been used for Electroencephalogram (EEG)-based diagnosis of various neur...

Inferring Clinical Correlations from EEG Reports with Deep Neural Learning.

Successful diagnosis and management of neurological dysfunction relies on proper communication between the neurologist and the primary physician (or o...

Deep Learning Meets Biomedical Ontologies: Knowledge Embeddings for Epilepsy.

While biomedical ontologies have traditionally been used to guide the identification of concepts or relations in biomedical data, recent advances in d...

Control of a 7-DOF Robotic Arm System With an SSVEP-Based BCI.

Although robot technology has been successfully used to empower people who suffer from motor disabilities to increase their interaction with their phy...

A Novel Method of Building Functional Brain Network Using Deep Learning Algorithm with Application in Proficiency Detection.

Functional brain network (FBN) has become very popular to analyze the interaction between cortical regions in the last decade. But researchers always ...

Automated detection of focal cortical dysplasia type II with surface-based magnetic resonance imaging postprocessing and machine learning.

OBJECTIVE: Focal cortical dysplasia (FCD) is a major pathology in patients undergoing surgical resection to treat pharmacoresistant epilepsy. Magnetic...

Neonatal Seizure Detection Using Deep Convolutional Neural Networks.

Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published st...

Ongoing brain rhythms shape I-wave properties in a computational model.

BACKGROUND: Responses to transcranial magnetic stimulation (TMS) are notoriously variable. Previous studies have observed a dependence of TMS-induced ...

Relevant Feature Selection from a Combination of Spectral-Temporal and Spatial Features for Classification of Motor Imagery EEG.

This paper presents a novel algorithm (CVSTSCSP) for determining discriminative features from an optimal combination of temporal, spectral and spatial...

Electroencephalographic derived network differences in Lewy body dementia compared to Alzheimer's disease patients.

Dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) require differential management despite presenting with symptomatic overlap. Currently, t...

Postoperative seizure outcome-guided machine learning for interictal electrocorticography in neocortical epilepsy.

The objective of this study was to introduce a new machine learning guided by outcome of resective epilepsy surgery defined as the presence/absence of...

Classifying dynamic transitions in high dimensional neural mass models: A random forest approach.

Neural mass models (NMMs) are increasingly used to uncover the large-scale mechanisms of brain rhythms in health and disease. The dynamics of these mo...

Deep learning for hybrid EEG-fNIRS brain-computer interface: application to motor imagery classification.

OBJECTIVE: Brain-computer interface (BCI) refers to procedures that link the central nervous system to a device. BCI was historically performed using ...

Using Dictionary Pair Learning for Seizure Detection.

Automatic seizure detection is extremely important in the monitoring and diagnosis of epilepsy. The paper presents a novel method based on dictionary ...

EmotionMeter: A Multimodal Framework for Recognizing Human Emotions.

In this paper, we present a multimodal emotion recognition framework called EmotionMeter that combines brain waves and eye movements. To increase the ...

Anytime multipurpose emotion recognition from EEG data using a Liquid State Machine based framework.

Recent technological advances in machine learning offer the possibility of decoding complex datasets and discern latent patterns. In this study, we ad...

Machine Learning EEG to Predict Cognitive Functioning and Processing Speed Over a 2-Year Period in Multiple Sclerosis Patients and Controls.

Event-related potentials (ERPs) show promise to be objective indicators of cognitive functioning. The aim of the study was to examine if ERPs recorded...

Classification of Pre-Clinical Seizure States Using Scalp EEG Cross-Frequency Coupling Features.

OBJECTIVE: This work proposes a machine-learning based system for a scalp EEG that flags an alarm in advance of a clinical seizure onset.