Seizures

Multi optimized SVM classifiers for motor imagery left and right hand movement identification.

EEG signal can be a good alternative for disabled persons who cannot perform actions or perform them improperly. Brain computer interface (BCI) is an attractive technology which permits control and interaction with a computer or a machine using EEG signals. Brain task identification based on EEG signals is very difficult task and is still challenging researchers. In this paper, the motor imagery o...

A Multi-Branch 3D Convolutional Neural Network for EEG-Based Motor Imagery Classification.

One of the challenges in motor imagery (MI) classification tasks is finding an easy-handled electroencephalogram (EEG) representation method which can preserve not only temporal features but also spatial ones. To fully utilize the features on various dimensions of EEG, a novel MI classification framework is first introduced in this paper, including a new 3D representation of EEG, a multi-branch 3D...

Connectomic Profiling Identifies Responders to Vagus Nerve Stimulation.

OBJECTIVE: Vagus nerve stimulation (VNS) is a common treatment for medically intractable epilepsy, but response rates are highly variable, with no pre...

Active deep learning for the identification of concepts and relations in electroencephalography reports.

The identification of medical concepts, their attributes and the relations between concepts in a large corpus of Electroencephalography (EEG) reports ...

Investigation of bias in an epilepsy machine learning algorithm trained on physician notes.

Racial disparities in the utilization of epilepsy surgery are well documented, but it is unknown whether a natural language processing (NLP) algorithm...

Using scalp EEG and intracranial EEG signals for predicting epileptic seizures: Review of available methodologies.

Patients suffering from epileptic seizures are usually treated with medication and/or surgical procedures. However, in more than 30% of cases, medicat...

Deep learning-based electroencephalography analysis: a systematic review.

CONTEXT: Electroencephalography (EEG) is a complex signal and can require several years of training, as well as advanced signal processing and feature...

Early prediction of epileptic seizures using a long-term recurrent convolutional network.

BACKGROUND: A seizure prediction system can detect seizures prior to their occurrence and allow clinicians to provide timely treatment for patients wi...

Constructing a Personalized Cross-Day EEG-Based Emotion-Classification Model Using Transfer Learning.

State-of-the-art electroencephalogram (EEG)-based emotion-classification works indicate that a personalized model may not be well exploited until suff...

A Real-Time Health 4.0 Framework with Novel Feature Extraction and Classification for Brain-Controlled IoT-Enabled Environments.

In this letter, we propose two novel methods for four-class motor imagery (MI) classification using electroencephalography (EEG). Also, we developed a...

Exploiting Graphoelements and Convolutional Neural Networks with Long Short Term Memory for Classification of the Human Electroencephalogram.

The electroencephalogram (EEG) is a cornerstone of neurophysiological research and clinical neurology. Historically, the classification of EEG as show...

Epilepsy Seizure Prediction on EEG Using Common Spatial Pattern and Convolutional Neural Network.

Epilepsy seizure prediction paves the way of timely warning for patients to take more active and effective intervention measures. Compared to seizure ...

Reachability Analysis of Neural Masses and Seizure Control Based on Combination Convolutional Neural Network.

Epileptic seizures arise from synchronous firing of multiple spatially separated neural masses; therefore, many synchrony measures are used for seizur...

Epileptic Seizure Detection with EEG Textural Features and Imbalanced Classification Based on EasyEnsemble Learning.

Imbalance data classification is a challenging task in automatic seizure detection from electroencephalogram (EEG) recordings when the durations of no...

Quantitative EEG reactivity and machine learning for prognostication in hypoxic-ischemic brain injury.

OBJECTIVE: Electroencephalogram (EEG) reactivity is a robust predictor of neurological recovery after cardiac arrest, however interrater-agreement amo...

Intracortical neural activity distal to seizure-onset-areas predicts human focal seizures.

The apparent unpredictability of epileptic seizures has a major impact in the quality of life of people with pharmacologically resistant seizures. Her...

Is it possible to detect cerebral dominance via EEG signals by using deep learning?

Each brain hemisphere is dominant for certain functions such as speech. The determination of speech laterality prior to surgery is of paramount import...

Efficient Epileptic Seizure Prediction Based on Deep Learning.

Epilepsy is one of the world's most common neurological diseases. Early prediction of the incoming seizures has a great influence on epileptic patient...

Depression recognition using machine learning methods with different feature generation strategies.

The diagnosis of depression almost exclusively depends on doctor-patient communication and scale analysis, which have the obvious disadvantages such a...

Correlation-based channel selection and regularized feature optimization for MI-based BCI.

Multi-channel EEG data are usually necessary for spatial pattern identification in motor imagery (MI)-based brain computer interfaces (BCIs). To some ...