Seizures

LightSeizureNet: A Lightweight Deep Learning Model for Real-Time Epileptic Seizure Detection.

The monitoring of epilepsy patients in non-hospital environment is highly desirable, where ultra-low...

Decoding study-independent mind-wandering from EEG using convolutional neural networks.

. Mind-wandering is a mental phenomenon where the internal thought process disengages from the exter...

Deep learning-based EEG analysis to classify normal, mild cognitive impairment, and dementia: Algorithms and dataset.

For automatic EEG diagnosis, this paper presents a new EEG data set with well-organized clinical ann...

EEG-Based Emotion Classification in Financial Trading Using Deep Learning: Effects of Risk Control Measures.

Day traders in the financial markets are under constant pressure to make rapid decisions and limit c...

From basic sciences and engineering to epileptology: A translational approach.

Collaborative efforts between basic scientists, engineers, and clinicians are enabling translational...

Classification of coma/brain-death EEG dataset based on one-dimensional convolutional neural network.

Electroencephalography (EEG) evaluation is an important step in the clinical diagnosis of brain deat...

Confused or not: decoding brain activity and recognizing confusion in reasoning learning using EEG.

Confusion is the primary epistemic emotion in the learning process, influencing students' engagement...

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 au...

Putting the "Big" in Big Data: Learning to Be Just as (Un)certain as a Clinician at EEG.

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 ...

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 ...

Anesthetic implications in the placement of deep brain electrodes through a stereotactic robot in the study of drug resistant epilepsy.

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 mak...

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...

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...

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...

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...

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 limi...

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...

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 bi...

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 ...