Seizures

Assessing the user experience of older adults using a neural network trained to recognize emotions from brain signals.

The use of Ambient Assisted Living (AAL) technologies as a means to cope with problems that arise due to an increasing and aging population is becoming usual. AAL technologies are used to prevent, cure and improve the wellness and health conditions of the elderly. However, their adoption and use by older adults is still a major challenge. User Experience (UX) evaluations aim at aiding on this task...

Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2-D cursor movements for BCI using SVM and ANN.

Brain computer interface (BCI) is a new communication way between man and machine. It identifies mental task patterns stored in electroencephalogram (EEG). So, it extracts brain electrical activities recorded by EEG and transforms them machine control commands. The main goal of BCI is to make available assistive environmental devices for paralyzed people such as computers and makes their life easi...

SVM-Based System for Prediction of Epileptic Seizures From iEEG Signal.

OBJECTIVE: This paper describes a data-analytic modeling approach for the prediction of epileptic seizures from intracranial electroencephalogram (iEE...

Seizure Forecasting and the Preictal State in Canine Epilepsy.

The ability to predict seizures may enable patients with epilepsy to better manage their medications and activities, potentially reducing side effects...

Real-time multi-channel monitoring of burst-suppression using neural network technology during pediatric status epilepticus treatment.

OBJECTIVE: To develop a real-time monitoring system that has the potential to guide the titration of anesthetic agents in the treatment of pediatric s...

Statistical Performance Analysis of Data-Driven Neural Models.

Data-driven model-based analysis of electrophysiological data is an emerging technique for understanding the mechanisms of seizures. Model-based analy...

Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization.

Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification per...

Validation of a novel classification model of psychogenic nonepileptic seizures by video-EEG analysis and a machine learning approach.

The aim of this study was to validate a novel classification for the diagnosis of PNESs. Fifty-five PNES video-EEG recordings were retrospectively ana...

Deep Learning Representation from Electroencephalography of Early-Stage Creutzfeldt-Jakob Disease and Features for Differentiation from Rapidly Progressive Dementia.

A novel technique of quantitative EEG for differentiating patients with early-stage Creutzfeldt-Jakob disease (CJD) from other forms of rapidly progre...

Pattern Classification of Instantaneous Cognitive Task-load Through GMM Clustering, Laplacian Eigenmap, and Ensemble SVMs.

The identification of the temporal variations in human operator cognitive task-load (CTL) is crucial for preventing possible accidents in human-machin...

Mapping dynamical properties of cortical microcircuits using robotized TMS and EEG: Towards functional cytoarchitectonics.

Brain dynamics at rest depend on the large-scale interactions between oscillating cortical microcircuits arranged into macrocolumns. Cytoarchitectonic...

Using ELM-based weighted probabilistic model in the classification of synchronous EEG BCI.

Extreme learning machine (ELM) is an effective machine learning technique with simple theory and fast implementation, which has gained increasing inte...

Sparse Bayesian Learning for Obtaining Sparsity of EEG Frequency Bands Based Feature Vectors in Motor Imagery Classification.

Effective common spatial pattern (CSP) feature extraction for motor imagery (MI) electroencephalogram (EEG) recordings usually depends on the filter b...

An EEG-Based Fuzzy Probability Model for Early Diagnosis of Alzheimer's Disease.

Alzheimer's disease is a degenerative brain disease that results in cardinal memory deterioration and significant cognitive impairments. The early tre...

Pattern recognition for electroencephalographic signals based on continuous neural networks.

This study reports the design and implementation of a pattern recognition algorithm to classify electroencephalographic (EEG) signals based on artific...

Optimal training dataset composition for SVM-based, age-independent, automated epileptic seizure detection.

Automated seizure detection is a valuable asset to health professionals, which makes adequate treatment possible in order to minimize brain damage. Mo...

Classifying Response Correctness across Different Task Sets: A Machine Learning Approach.

Erroneous behavior usually elicits a distinct pattern in neural waveforms. In particular, inspection of the concurrent recorded electroencephalograms ...

Switching EEG Headsets Made Easy: Reducing Offline Calibration Effort Using Active Weighted Adaptation Regularization.

Electroencephalography (EEG) headsets are the most commonly used sensing devices for brain-computer interface. In real-world applications, there are a...

Genes with high penetrance for syndromic and non-syndromic autism typically function within the nucleus and regulate gene expression.

BACKGROUND: Intellectual disability (ID), autism, and epilepsy share frequent yet variable comorbidities with one another. In order to better understa...

Detection of Shockable Ventricular Arrhythmia using Variational Mode Decomposition.

Ventricular tachycardia (VT) and ventricular fibrillation (VF) are shockable ventricular cardiac ailments. Detection of VT/VF is one of the important ...