Seizures

A Biologically Inspired Approach to Frequency Domain Feature Extraction for EEG Classification.

Classification of electroencephalogram (EEG) signal is important in mental decoding for brain-computer interfaces (BCI). We introduced a feature extraction approach based on frequency domain analysis to improve the classification performance on different mental tasks using single-channel EEG. This biologically inspired method extracts the most discriminative spectral features from power spectral d...

Random ensemble learning for EEG classification.

Real-time detection of seizure activity in epilepsy patients is critical in averting seizure activity and improving patients' quality of life. Accurate evaluation, presurgical assessment, seizure prevention, and emergency alerts all depend on the rapid detection of seizure onset. A new method of feature selection and classification for rapid and precise seizure detection is discussed wherein infor...

Feature Extraction with GMDH-Type Neural Networks for EEG-Based Person Identification.

The brain activity observed on EEG electrodes is influenced by volume conduction and functional connectivity of a person performing a task. When the t...

Focal Onset Seizure Prediction Using Convolutional Networks.

OBJECTIVE: This paper investigates the hypothesis that focal seizures can be predicted using scalp electroencephalogram (EEG) data. Our first aim is t...

Epileptic seizure detection in EEG signal using machine learning techniques.

Epilepsy is a well-known nervous system disorder characterized by seizures. Electroencephalograms (EEGs), which capture brain neural activity, can det...

Epileptic Seizures Prediction Using Machine Learning Methods.

Epileptic seizures occur due to disorder in brain functionality which can affect patient's health. Prediction of epileptic seizures before the beginni...

Epileptic seizure detection using DWT-based approximate entropy, Shannon entropy and support vector machine: a case study.

In this work, we have used a time-frequency domain analysis method called discrete wavelet transform (DWT) technique. This method stand out compared t...

Intrinsic interactive reinforcement learning - Using error-related potentials for real world human-robot interaction.

Reinforcement learning (RL) enables robots to learn its optimal behavioral strategy in dynamic environments based on feedback. Explicit human feedback...

Epileptic Seizure Prediction Using Big Data and Deep Learning: Toward a Mobile System.

BACKGROUND: Seizure prediction can increase independence and allow preventative treatment for patients with epilepsy. We present a proof-of-concept fo...

Bullying incidences identification within an immersive environment using HD EEG-based analysis: A Swarm Decomposition and Deep Learning approach.

Bullying is an everlasting phenomenon and the first, yet difficult, step towards the solution is its detection. Conventional approaches for bullying i...

An EEG-based functional connectivity measure for automatic detection of alcohol use disorder.

BACKGROUND: The abnormal alcohol consumption could cause toxicity and could alter the human brain's structure and function, termed as alcohol used dis...

Code-modulated visual evoked potentials using fast stimulus presentation and spatiotemporal beamformer decoding.

When encoding visual targets using various lagged versions of a pseudorandom binary sequence of luminance changes, the EEG signal recorded over the vi...

Convolutional Neural Networks with 3D Input for P300 Identification in Auditory Brain-Computer Interfaces.

From allowing basic communication to move through an environment, several attempts are being made in the field of brain-computer interfaces (BCI) to a...

Quantitative surface analysis of combined MRI and PET enhances detection of focal cortical dysplasias.

OBJECTIVE: Focal cortical dysplasias (FCDs) often cause pharmacoresistant epilepsy, and surgical resection can lead to seizure-freedom. Magnetic reson...

CSP-TSM: Optimizing the performance of Riemannian tangent space mapping using common spatial pattern for MI-BCI.

BACKGROUND: Classification of electroencephalography (EEG) signals for motor imagery based brain computer interface (MI-BCI) is an exigent task and co...

Towards multilevel mental stress assessment using SVM with ECOC: an EEG approach.

Mental stress has been identified as one of the major contributing factors that leads to various diseases such as heart attack, depression, and stroke...

Improving the Accuracy and Training Speed of Motor Imagery Brain-Computer Interfaces Using Wavelet-Based Combined Feature Vectors and Gaussian Mixture Model-Supervectors.

In this paper, we propose a set of wavelet-based combined feature vectors and a Gaussian mixture model (GMM)-supervector to enhance training speed and...

Automated diagnosis of temporal lobe epilepsy in the absence of interictal spikes.

OBJECTIVE: To diagnose and lateralise temporal lobe epilepsy (TLE) by building a classification system that uses directed functional connectivity patt...

Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals.

An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of epilepsy. The EEG signal contains information about the electrica...

Detection of Interictal Discharges With Convolutional Neural Networks Using Discrete Ordered Multichannel Intracranial EEG.

Detection algorithms for electroencephalography (EEG) data, especially in the field of interictal epileptiform discharge (IED) detection, have traditi...