Nonlinear dynamical analysis of EEG and MEG: review of an emerging field
CJ Stam - Clinical neurophysiology, 2005 - Elsevier
Many complex and interesting phenomena in nature are due to nonlinear phenomena. The
theory of nonlinear dynamical systems, also called 'chaos theory', has now progressed to a …
theory of nonlinear dynamical systems, also called 'chaos theory', has now progressed to a …
Machine learning for predicting epileptic seizures using EEG signals: A review
With the advancement in artificial intelligence (AI) and machine learning (ML) techniques,
researchers are striving towards employing these techniques for advancing clinical practice …
researchers are striving towards employing these techniques for advancing clinical practice …
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 signal is generally chosen from a …
using neural activity as the control signal. This neural signal is generally chosen from a …
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 imminent seizure. In this …
capture changes in electrical brain activity that could declare an imminent seizure. In this …
Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study
Background Seizure prediction would be clinically useful in patients with epilepsy and could
improve safety, increase independence, and allow acute treatment. We did a multicentre …
improve safety, increase independence, and allow acute treatment. We did a multicentre …
Seizure prediction: the long and winding road
The sudden and apparently unpredictable nature of seizures is one of the most disabling
aspects of the disease epilepsy. A method capable of predicting the occurrence of seizures …
aspects of the disease epilepsy. A method capable of predicting the occurrence of seizures …
Application of machine learning to epileptic seizure onset detection and treatment
AH Shoeb - 2009 - dspace.mit.edu
Epilepsy is a chronic disorder of the central nervous system that predisposes individuals to
experiencing recurrent seizures. It affects 3 million Americans and 50 million people world …
experiencing recurrent seizures. It affects 3 million Americans and 50 million people world …
Applying deep learning for epilepsy seizure detection and brain map** visualization
Deep Convolutional Neural Network (CNN) has achieved remarkable results in computer
vision tasks for end-to-end learning. We evaluate here the power of a deep CNN to learn …
vision tasks for end-to-end learning. We evaluate here the power of a deep CNN to learn …
[HTML][HTML] Mental health monitoring with multimodal sensing and machine learning: A survey
Personal and ubiquitous sensing technologies such as smartphones have allowed the
continuous collection of data in an unobtrusive manner. Machine learning methods have …
continuous collection of data in an unobtrusive manner. Machine learning methods have …
Adult epilepsy
The epilepsies are one of the most common serious brain disorders, can occur at all ages,
and have many possible presentations and causes. Although incidence in childhood has …
and have many possible presentations and causes. Although incidence in childhood has …