Seizures

Sample entropy analysis of EEG signals via artificial neural networks to model patients' consciousness level based on anesthesiologists experience.

Electroencephalogram (EEG) signals, as it can express the human brain's activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA). Bispectral (BIS) index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this ...

Using trend templates in a neonatal seizure algorithm improves detection of short seizures in a foetal ovine model.

Seizures below one minute in duration are difficult to assess correctly using seizure detection algorithms. We aimed to improve neonatal detection algorithm performance for short seizures through the use of trend templates for seizure onset and end. Bipolar EEG were recorded within a transiently asphyxiated ovine model at 0.7 gestational age, a common experimental model for studying brain developm...

Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques.

This paper describes a discrete wavelet transform-based feature extraction scheme for the classification of EEG signals. In this scheme, the discrete ...

Automatic epileptic seizure detection using scalp EEG and advanced artificial intelligence techniques.

The epilepsies are a heterogeneous group of neurological disorders and syndromes characterised by recurrent, involuntary, paroxysmal seizure activity,...

A Novel Method for Automated Diagnosis of Epilepsy Using Complex-Valued Classifiers.

The study reported herein proposes a new method for the diagnosis of epilepsy from electroencephalography (EEG) signals based on complex classifiers. ...

Kernel collaborative representation-based automatic seizure detection in intracranial EEG.

Automatic seizure detection is of great significance in the monitoring and diagnosis of epilepsy. In this study, a novel method is proposed for automa...

Determining the appropriate amount of anesthetic gas using DWT and EMD combined with neural network.

The spectrum of EEG has been studied to predict the depth of anesthesia using variety of signal processing methods up to date. Those standard models h...

A novel EOG/EEG hybrid human-machine interface adopting eye movements and ERPs: application to robot control.

This study presents a novel human-machine interface (HMI) based on both electrooculography (EOG) and electroencephalography (EEG). This hybrid interfa...

Using Relevance Feedback to Distinguish the Changes in EEG During Different Absence Seizure Phases.

We carried out a series of statistical experiments to explore the utility of using relevance feedback on electroencephalogram (EEG) data to distinguis...

Electroencephalographic Changes of Brain Oscillatory Activity After Upper Limb Somatic Sensation Training in a Patient With Somatosensory Deficit After Stroke.

The development of an innovative functional assessment procedure based on the combination of electroencephalography (EEG) and robot-assisted upper lim...

A machine learning approach using auditory odd-ball responses to investigate the effect of Clozapine therapy.

OBJECTIVE: To develop a machine learning (ML) methodology based on features extracted from odd-ball auditory evoked potentials to identify neurophysio...

Detection of temporal lobe epilepsy using support vector machines in multi-parametric quantitative MR imaging.

The detection of MRI abnormalities that can be associated to seizures in the study of temporal lobe epilepsy (TLE) is a challenging task. In many case...

Artificial neural networks in neurosurgery.

Artificial neural networks (ANNs) effectively analyze non-linear data sets. The aimed was A review of the relevant published articles that focused on ...

Epileptic seizure prediction using relative spectral power features.

OBJECTIVE: Prediction of epileptic seizures can improve the living conditions for refractory epilepsy patients. We aimed to improve sensitivity and sp...

Time-frequency modulation of ERD and EEG coherence in robot-assisted hand performance.

A better understanding of cortical modifications related to movement preparation and execution after robot-assisted training could aid in refining reh...

Decoding objects of basic categories from electroencephalographic signals using wavelet transform and support vector machines.

Decoding and classification of objects through task-oriented electroencephalographic (EEG) signals are the most crucial goals of recent researches con...

A Randomized Controlled Trial of EEG-Based Motor Imagery Brain-Computer Interface Robotic Rehabilitation for Stroke.

Electroencephalography (EEG)-based motor imagery (MI) brain-computer interface (BCI) technology has the potential to restore motor function by inducin...

Feature Selection and Classification of Electroencephalographic Signals: An Artificial Neural Network and Genetic Algorithm Based Approach.

Feature selection is an important step in many pattern recognition systems aiming to overcome the so-called curse of dimensionality. In this study, an...

Decoding Agency-Related Neural States During Human-AI Interaction in Autonomous Driving Using EEG and Deep Learning

The sense of agency, the experience of controlling one's actions and their consequences, is a fundamental component of human interaction with autonomo...

Real-Time EEG Cap Electrode Detection for Guided Point-of-Care Placement

We present a two-stage vision system that detects EEG cap electrodes in a live webcam stream and validates their anatomical placement in real time. A ...