Seizures

A New Approach on HCI Extracting Conscious Jaw Movements Based on EEG Signals Using Machine Learnings.

Machine computer interfaces (MCI) are assistive technologies enabling paralyzed peoples to control and communicate their environments. This study aims to discover and represents a new approach on MCI using left/right motions of voluntary jaw movements stored in electroencephalogram (EEG). It extracts brain electrical activities on EEG produced by voluntary jaw movements and converts these activiti...

Machine learning as a new paradigm for characterizing localization and lateralization of neuropsychological test data in temporal lobe epilepsy.

In this study, we employed a kernel support vector machine to predict epilepsy localization and lateralization for patients with a diagnosis of epilepsy (n = 228). We assessed the accuracy to which indices of verbal memory, visual memory, verbal fluency, and naming would localize and lateralize seizure focus in comparison to standard electroencephalogram (EEG). Classification accuracy was defined ...

A hierarchical semi-supervised extreme learning machine method for EEG recognition.

Feature extraction and classification is a vital part in motor imagery-based brain-computer interface (BCI) system. Traditional deep learning (DL) met...

A Systematic Review for Human EEG Brain Signals Based Emotion Classification, Feature Extraction, Brain Condition, Group Comparison.

The study of electroencephalography (EEG) signals is not a new topic. However, the analysis of human emotions upon exposure to music considered as imp...

Nonlinear effective connectivity measure based on adaptive Neuro Fuzzy Inference System and Granger Causality.

Exploring brain networks is an essential step towards understanding functional organization of the brain, which needs characterization of linear and n...

Language processing from the perspective of electrical stimulation mapping.

Electrical Stimulation (ES) is a neurostimulation technique that is used to localize language functions in the brain of people with intractable epilep...

Global temporal lobe asymmetry as a semi-quantitative imaging biomarker for temporal lobe epilepsy lateralization: A machine learning classification study.

OBJECTIVE: The purpose of this study was to evaluate the utility of global semi-quantitative analysis via fluorine-18-flurodeoxyglucose positron emiss...

Machine learning detects EEG microstate alterations in patients living with temporal lobe epilepsy.

PURPOSE: Quasi-stable electrical distribution in EEG called microstates could carry useful information on the dynamics of large scale brain networks. ...

Deep learning for detection of focal epileptiform discharges from scalp EEG recordings.

OBJECTIVE: Visual assessment of the EEG still outperforms current computer algorithms in detecting epileptiform discharges. Deep learning is a promisi...

Introducing chaos behavior to kernel relevance vector machine (RVM) for four-class EEG classification.

This paper addresses a chaos kernel function for the relevance vector machine (RVM) in EEG signal classification, which is an important component of B...

Transductive Joint-Knowledge-Transfer TSK FS for Recognition of Epileptic EEG Signals.

Intelligent recognition of electroencephalogram (EEG) signals is an important means to detect seizure. Traditional methods for recognizing epileptic E...

Time-Varying EEG Correlations Improve Automated Neonatal Seizure Detection.

The aim of this study was to develop methods for detecting the nonstationary periodic characteristics of neonatal electroencephalographic (EEG) seizur...

Automated detection of electroencephalography artifacts in human, rodent and canine subjects using machine learning.

BACKGROUND: Electroencephalography (EEG) invariably contains extra-cranial artifacts that are commonly dealt with based on qualitative and subjective ...

EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces.

OBJECTIVE: Brain-computer interfaces (BCI) enable direct communication with a computer, using neural activity as the control signal. This neural signa...

Integrating artificial intelligence with real-time intracranial EEG monitoring to automate interictal identification of seizure onset zones in focal epilepsy.

OBJECTIVE: An ability to map seizure-generating brain tissue, i.e. the seizure onset zone (SOZ), without recording actual seizures could reduce the du...

Epileptic seizure anticipation and localisation of epileptogenic region using EEG signals.

Electric activity of brain gets disturbed prior to epileptic seizure onset. Early prediction of an upcoming seizure can help to increase effectiveness...

Prediction of central neuropathic pain in spinal cord injury based on EEG classifier.

OBJECTIVES: To create a classifier based on electroencephalography (EEG) to identify spinal cord injured (SCI) participants at risk of developing cent...

Tensor-driven extraction of developmental features from varying paediatric EEG datasets.

OBJECTIVE: Constant changes in developing children's brains can pose a challenge in EEG dependant technologies. Advancing signal processing methods to...

A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals.

The electroencephalogram (EEG) is the most prominent means to study epilepsy and capture changes in electrical brain activity that could declare an im...

Differential Effects of Simulated Cortical Network Lesions on Synchrony and EEG Complexity.

Brain function has been proposed to arise as a result of the coordinated activity between distributed brain areas. An important issue in the study of ...