Seizures

Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction: a review.

Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study...

EEG artifacts reduction by multivariate empirical mode decomposition and multiscale entropy for monitoring depth of anaesthesia during surgery.

Electroencephalography (EEG) has been widely utilized to measure the depth of anaesthesia (DOA) during operation. However, the EEG signals are usually contaminated by artifacts which have a consequence on the measured DOA accuracy. In this study, an effective and useful filtering algorithm based on multivariate empirical mode decomposition and multiscale entropy (MSE) is proposed to measure DOA. M...

Metrics of brain network architecture capture the impact of disease in children with epilepsy.

BACKGROUND AND OBJECTIVE: Epilepsy is associated with alterations in the structural framework of the cerebral network. The aim of this study was to me...

A novel deep learning approach for classification of EEG motor imagery signals.

OBJECTIVE: Signal classification is an important issue in brain computer interface (BCI) systems. Deep learning approaches have been used successfully...

EEG and fMRI agree: Mental arithmetic is the easiest form of imagery to detect.

fMRI and EEG during mental imagery provide alternative methods of detecting awareness in patients with disorders of consciousness (DOC) without relian...

Quantitative EEG Evaluation During Robot-Assisted Foot Movement.

Passiveand imagined limbmovements induce changes in cerebral oscillatory activity. Central modulatory effects play a role in plastic changes, and are ...

Scale-Dependent Signal Identification in Low-Dimensional Subspace: Motor Imagery Task Classification.

Motor imagery electroencephalography (EEG) has been successfully used in locomotor rehabilitation programs. While the noise-assisted multivariate empi...

An EEG-based machine learning method to screen alcohol use disorder.

Screening alcohol use disorder (AUD) patients has been challenging due to the subjectivity involved in the process. Hence, robust and objective method...

Correlated EEG Signals Simulation Based on Artificial Neural Networks.

In recent years, simulation of the human electroencephalogram (EEG) data found its important role in medical domain and neuropsychology. In this paper...

A Realistic Seizure Prediction Study Based on Multiclass SVM.

A patient-specific algorithm, for epileptic seizure prediction, based on multiclass support-vector machines (SVM) and using multi-channel high-dimensi...

LMD Based Features for the Automatic Seizure Detection of EEG Signals Using SVM.

Achieving the goal of detecting seizure activity automatically using electroencephalogram (EEG) signals is of great importance and significance for th...

A Cross-Correlated Delay Shift Supervised Learning Method for Spiking Neurons with Application to Interictal Spike Detection in Epilepsy.

This study introduces a novel learning algorithm for spiking neurons, called CCDS, which is able to learn and reproduce arbitrary spike patterns in a ...

Epileptic Focus Localization Using Discrete Wavelet Transform Based on Interictal Intracranial EEG.

Over the past decade, with the development of machine learning, discrete wavelet transform (DWT) has been widely used in computer-aided epileptic elec...

Robust Wavelet Stabilized 'Footprints of Uncertainty' for Fuzzy System Classifiers to Automatically Detect Sharp Waves in the EEG after Hypoxia Ischemia.

Currently, there are no developed methods to detect sharp wave transients that exist in the latent phase after hypoxia-ischemia (HI) in the electroenc...

A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines.

Motor imagery classification is an important topic in brain-computer interface (BCI) research that enables the recognition of a subject's intension to...

Detecting epileptic seizures with electroencephalogram via a context-learning model.

BACKGROUND: Epileptic seizure is a serious health problem in the world and there is a huge population suffering from it every year. If an algorithm co...

Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators.

Early recognition of ventricular fibrillation (VF) and electrical therapy are key for the survival of out-of-hospital cardiac arrest (OHCA) patients t...

Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features.

Recently, an increasing number of researchers have endeavored to develop practical tools for diagnosing patients with schizophrenia using machine lear...

Classification Preictal and Interictal Stages via Integrating Interchannel and Time-Domain Analysis of EEG Features.

The life quality of patients with refractory epilepsy is extremely affected by abrupt and unpredictable seizures. A reliable method for predicting sei...

Motor Imagery Classification Based on Bilinear Sub-Manifold Learning of Symmetric Positive-Definite Matrices.

In motor imagery brain-computer interfaces (BCIs), the symmetric positive-definite (SPD) covariance matrices of electroencephalogram (EEG) signals car...