Seizures

A Correlation-Driven Mapping For Deep Learning application in detecting artifacts within the EEG.

OBJECTIVE: When developing approaches for automatic preprocessing of electroencephalogram (EEG) signals in non-isolated demanding environment such as intensive care unit (ICU) or even outdoor environment, one of the major concerns is varying nature of characteristics of different artifacts in time, frequency and spatial domains, which in turn causes a simple approach to be not enough for reliable ...

Data augmentation for enhancing EEG-based emotion recognition with deep generative models.

OBJECTIVE: The data scarcity problem in emotion recognition from electroencephalography (EEG) leads to difficulty in building an affective model with high accuracy using machine learning algorithms or deep neural networks. Inspired by emerging deep generative models, we propose three methods for augmenting EEG training data to enhance the performance of emotion recognition models.

An automatic EEG-based sleep staging system with introducing NAoSP and NAoGP as new metrics for sleep staging systems.

Different biological signals are recorded in sleep labs during sleep for the diagnosis and treatment of human sleep problems. Classification of sleep ...

Machine learning from wristband sensor data for wearable, noninvasive seizure forecasting.

OBJECTIVE: Seizure forecasting may provide patients with timely warnings to adapt their daily activities and help clinicians deliver more objective, p...

DDxNet: a deep learning model for automatic interpretation of electronic health records, electrocardiograms and electroencephalograms.

Effective patient care mandates rapid, yet accurate, diagnosis. With the abundance of non-invasive diagnostic measurements and electronic health recor...

EEG-Based Epilepsy Recognition via Multiple Kernel Learning.

In the field of brain-computer interfaces, it is very common to use EEG signals for disease diagnosis. In this study, a style regularized least square...

FusionSense: Emotion Classification Using Feature Fusion of Multimodal Data and Deep Learning in a Brain-Inspired Spiking Neural Network.

Using multimodal signals to solve the problem of emotion recognition is one of the emerging trends in affective computing. Several studies have utiliz...

EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities.

Emotions are fundamental for human beings and play an important role in human cognition. Emotion is commonly associated with logical decision making, ...

Classification of Non-Severe Traumatic Brain Injury from Resting-State EEG Signal Using LSTM Network with ECOC-SVM.

Traumatic brain injury (TBI) is one of the common injuries when the human head receives an impact due to an accident or fall and is one of the most fr...

Improved Activity Recognition Combining Inertial Motion Sensors and Electroencephalogram Signals.

Human activity recognition and neural activity analysis are the basis for human computational neureoethology research dealing with the simultaneous an...

Person identification from EEG using various machine learning techniques with inter-hemispheric amplitude ratio.

Association between electroencephalography (EEG) and individually personal information is being explored by the scientific community. Though person id...

Temporal Lobe Epilepsy Surgical Outcomes Can Be Inferred Based on Structural Connectome Hubs: A Machine Learning Study.

OBJECTIVE: Medial temporal lobe epilepsy (TLE) is the most common form of medication-resistant focal epilepsy in adults. Despite removal of medial tem...

Instance Transfer Subject-Dependent Strategy for Motor Imagery Signal Classification Using Deep Convolutional Neural Networks.

In the process of brain-computer interface (BCI), variations across sessions/subjects result in differences in the properties of potential of the brai...

A machine-learning algorithm for neonatal seizure recognition: a multicentre, randomised, controlled trial.

BACKGROUND: Despite the availability of continuous conventional electroencephalography (cEEG), accurate diagnosis of neonatal seizures is challenging ...

Deep learning and feature based medication classifications from EEG in a large clinical data set.

The amount of freely available human phenotypic data is increasing daily, and yet little is known about the types of inferences or identifying charact...

Machine learning for a combined electroencephalographic anesthesia index to detect awareness under anesthesia.

Spontaneous electroencephalogram (EEG) and auditory evoked potentials (AEP) have been suggested to monitor the level of consciousness during anesthesi...

The EEG Signal Analysis for Spatial Cognitive Ability Evaluation Based on Multivariate Permutation Conditional Mutual Information-Multi-Spectral Image.

This study aims to find an effective method to evaluate the efficacy of cognitive training of spatial memory under a virtual reality environment, by c...

Linear predictive coding distinguishes spectral EEG features of Parkinson's disease.

OBJECTIVE: We have developed and validated a novel EEG-based signal processing approach to distinguish PD and control patients: Linear-predictive-codi...

Emotional EEG classification using connectivity features and convolutional neural networks.

Convolutional neural networks (CNNs) are widely used to recognize the user's state through electroencephalography (EEG) signals. In the previous studi...

Changes in electroencephalography complexity and functional magnetic resonance imaging connectivity following robotic hand training in chronic stroke.

In recent years, robotic training has been utilized for recovery of motor control in patients with motor deficits. Along with clinical assessment, el...