Seizures

Multi-channel EEG-based sleep stage classification with joint collaborative representation and multiple kernel learning.

BACKGROUND: Electroencephalography (EEG) based sleep staging is commonly used in clinical routine. Feature extraction and representation plays a crucial role in EEG-based automatic classification of sleep stages. Sparse representation (SR) is a state-of-the-art unsupervised feature learning method suitable for EEG feature representation.

A Generalizable Brain-Computer Interface (BCI) Using Machine Learning for Feature Discovery.

This work describes a generalized method for classifying motor-related neural signals for a brain-computer interface (BCI), based on a stochastic machine learning method. The method differs from the various feature extraction and selection techniques employed in many other BCI systems. The classifier does not use extensive a-priori information, resulting in reduced reliance on highly specific doma...

Multimodal data and machine learning for surgery outcome prediction in complicated cases of mesial temporal lobe epilepsy.

BACKGROUND: This study sought to predict postsurgical seizure freedom from pre-operative diagnostic test results and clinical information using a rapi...

Multimodal predictor of neurodevelopmental outcome in newborns with hypoxic-ischaemic encephalopathy.

Automated multimodal prediction of outcome in newborns with hypoxic-ischaemic encephalopathy is investigated in this work. Routine clinical measures a...

EMD-Based Temporal and Spectral Features for the Classification of EEG Signals Using Supervised Learning.

This paper presents a novel method for feature extraction from electroencephalogram (EEG) signals using empirical mode decomposition (EMD). Its use is...

Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data.

The objective of this study is to evaluate machine learning algorithms aimed at predicting surgical treatment outcomes in groups of patients with temp...

Grading hypoxic-ischemic encephalopathy severity in neonatal EEG using GMM supervectors and the support vector machine.

OBJECTIVE: This work presents a novel automated system to classify the severity of hypoxic-ischemic encephalopathy (HIE) in neonates using EEG.

Cortical feature analysis and machine learning improves detection of "MRI-negative" focal cortical dysplasia.

Focal cortical dysplasia (FCD) is the most common cause of pediatric epilepsy and the third most common lesion in adults with treatment-resistant epil...

A Fuzzy-Based Fusion Method of Multimodal Sensor-Based Measurements for the Quantitative Evaluation of Eye Fatigue on 3D Displays.

With the rapid increase of 3-dimensional (3D) content, considerable research related to the 3D human factor has been undertaken for quantitatively eva...

On the proper selection of preictal period for seizure prediction.

Supervised machine learning-based seizure prediction methods consider preictal period as an important prerequisite parameter during training. However,...

A neural network-based optimal spatial filter design method for motor imagery classification.

In this study, a novel spatial filter design method is introduced. Spatial filtering is an important processing step for feature extraction in motor i...

Impaired dendritic inhibition leads to epileptic activity in a computer model of CA3.

Temporal lobe epilepsy (TLE) is a common type of epilepsy with hippocampus as the usual site of origin. The CA3 subfield of hippocampus is reported to...

Monitoring Neuro-Motor Recovery From Stroke With High-Resolution EEG, Robotics and Virtual Reality: A Proof of Concept.

A novel system for the neuro-motor rehabilitation of upper limbs was validated in three sub-acute post-stroke patients. The system permits synchronize...

Feature Selection Applying Statistical and Neurofuzzy Methods to EEG-Based BCI.

This paper presents an investigation aimed at drastically reducing the processing burden required by motor imagery brain-computer interface (BCI) syst...

Analysis of connectivity in NeuCube spiking neural network models trained on EEG data for the understanding of functional changes in the brain: A case study on opiate dependence treatment.

The paper presents a methodology for the analysis of functional changes in brain activity across different conditions and different groups of subjects...

Multifractal Analysis and Relevance Vector Machine-Based Automatic Seizure Detection in Intracranial EEG.

Automatic seizure detection technology is of great significance for long-term electroencephalogram (EEG) monitoring of epilepsy patients. The aim of t...

Support vector machine and fuzzy C-mean clustering-based comparative evaluation of changes in motor cortex electroencephalogram under chronic alcoholism.

In this study, the magnitude and spatial distribution of frequency spectrum in the resting electroencephalogram (EEG) were examined to address the pro...

Language-Model Assisted Brain Computer Interface for Typing: A Comparison of Matrix and Rapid Serial Visual Presentation.

Noninvasive electroencephalography (EEG)-based brain-computer interfaces (BCIs) popularly utilize event-related potential (ERP) for intent detection. ...

RSTFC: A Novel Algorithm for Spatio-Temporal Filtering and Classification of Single-Trial EEG.

Learning optimal spatio-temporal filters is a key to feature extraction for single-trial electroencephalogram (EEG) classification. The challenges are...

Automatic detection of sleep apnea based on EEG detrended fluctuation analysis and support vector machine.

Sleep apnea syndrome (SAS) is prevalent in individuals and recently, there are many studies focus on using simple and efficient methods for SAS detect...