Seizures

A multi-task and multi-channel convolutional neural network for semi-supervised neonatal artefact detection.

. Automated artefact detection in the neonatal electroencephalogram (EEG) is crucial for reliable automated EEG analysis, but limited availability of expert artefact annotations challenges the development of deep learning models for artefact detection. This paper proposes a semi-supervised deep learning approach for artefact detection in neonatal EEG that requires few labelled data by training a m...

Raw Electroencephalogram-Based Cognitive Workload Classification Using Directed and Nondirected Functional Connectivity Analysis and Deep Learning.

With the phenomenal rise in internet-of-things devices, the use of electroencephalogram (EEG) based brain-computer interfaces (BCIs) can empower individuals to control equipment with thoughts. These allow BCI to be used and pave the way for pro-active health management and the development of internet-of-medical-things architecture. However, EEG-based BCIs have low fidelity, high variance, and EEG ...

Discriminating and understanding brain states in children with epileptic spasms using deep learning and graph metrics analysis of brain connectivity.

BACKGROUND AND OBJECTIVE: Epilepsy is a brain disorder consisting of abnormal electrical discharges of neurons resulting in epileptic seizures. The na...

Multi-band spatial feature extraction and classification for motor imaging EEG signals based on OSFBCSP-GAO-SVM model : EEG signal processing.

Electroencephalogram (EEG) is a non-stationary random signal with strong background noise, which makes its feature extraction difficult and recognitio...

Cross-Domain Transfer of EEG to EEG or ECG Learning for CNN Classification Models.

Electroencephalography (EEG) is often used to evaluate several types of neurological brain disorders because of its noninvasive and high temporal reso...

Deep learning for the diagnosis of mesial temporal lobe epilepsy.

OBJECTIVE: This study aimed to enable the automatic detection of the hippocampus and diagnose mesial temporal lobe epilepsy (MTLE) with the hippocampu...

Neural stochastic differential equations network as uncertainty quantification method for EEG source localization.

EEG source localization remains a challenging problem given the uncertain conductivity values of the volume conductor models (VCMs). As uncertain cond...

An attention-based deep learning approach for the classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG.

. This study aims to design and implement the first deep learning (DL) model to classify subjects in the prodromic states of Alzheimer's disease (AD) ...

Leveraging Multiple Distinct EEG Training Sessions for Improvement of Spectral-Based Biometric Verification Results.

Most studies on EEG-based biometry recognition report results based on signal databases, with a limited number of recorded EEG sessions using the same...

Recent Advances in the Applications of Additive Manufacturing (3D Printing) in Drug Delivery: A Comprehensive Review.

There has been a tremendous increase in the investigations of three-dimensional (3D) printing for biomedical and pharmaceutical applications, and drug...

Classification of Motor Imagery EEG Signals Based on Data Augmentation and Convolutional Neural Networks.

In brain-computer interface (BCI) systems, motor imagery electroencephalography (MI-EEG) signals are commonly used to detect participant intent. Many ...

Mutual Information-Driven Subject-Invariant and Class-Relevant Deep Representation Learning in BCI.

In recent years, deep learning-based feature representation methods have shown a promising impact on electroencephalography (EEG)-based brain-computer...

Cognitive Depression Detection Cyber-Medical System Based on EEG Analysis and Deep Learning Approaches.

Long-term depression and negative emotional cycles affect life quality and work productivity. However, depression is not easy to detect, with current ...

A Spatiotemporal Graph Attention Network Based on Synchronization for Epileptic Seizure Prediction.

Accurate early prediction of epileptic seizures can provide timely treatment for patients. Previous studies have mainly focused on a single temporal o...

EEG emotion recognition using improved graph neural network with channel selection.

BACKGROUND AND OBJECTIVE: Emotion classification tasks based on electroencephalography (EEG) are an essential part of artificial intelligence, with pr...

An Efficient Machine Learning-Based Emotional Valence Recognition Approach Towards Wearable EEG.

Emotion artificial intelligence (AI) is being increasingly adopted in several industries such as healthcare and education. Facial expressions and tone...

Precise Discrimination for Multiple Etiologies of Dementia Cases Based on Deep Learning with Electroencephalography.

INTRODUCTION: It is critical to develop accurate and universally available biomarkers for dementia diseases to appropriately deal with the dementia pr...

Extracting a Novel Emotional EEG Topographic Map Based on a Stacked Autoencoder Network.

Emotion recognition based on brain signals has increasingly become attractive to evaluate human's internal emotional states. Conventional emotion reco...

Cross-task cognitive workload recognition using a dynamic residual network with attention mechanism based on neurophysiological signals.

BACKGROUND AND OBJECTIVE: Evaluation of human cognitive workload (CW) helps improve the user experience of human-centered systems. To provide a contin...

Prediction of Tinnitus Treatment Outcomes Based on EEG Sensors and TFI Score Using Deep Learning.

Tinnitus is a hearing disorder that is characterized by the perception of sounds in the absence of an external source. Currently, there is no pharmace...