Seizures

Design of EEG based thought identification system using EMD & deep neural network.

Biological communication system for neurological disorder patients is similar to the Brain Computer Interface in a way that it facilitates the connection to the outside world in real time. The interdisciplinary field of Electroencephalogram based message depiction is gaining importance as it assists the paralysed person to communicate. In the proposed method a novel approach of feature extraction ...

A protocol for trustworthy EEG decoding with neural networks.

Deep learning solutions have rapidly emerged for EEG decoding, achieving state-of-the-art performance on a variety of decoding tasks. Despite their high performance, existing solutions do not fully address the challenge posed by the introduction of many hyperparameters, defining data pre-processing, network architecture, network training, and data augmentation. Automatic hyperparameter search is r...

Review of deep representation learning techniques for brain-computer interfaces.

In the field of brain-computer interfaces (BCIs), the potential for leveraging deep learning techniques for representing electroencephalogram (EEG) si...

Decoding Brain Signals from Rapid-Event EEG for Visual Analysis Using Deep Learning.

The perception and recognition of objects around us empower environmental interaction. Harnessing the brain's signals to achieve this objective has co...

Application of Electroencephalography Sensors and Artificial Intelligence in Automated Language Teaching.

This study developed an automated language learning teaching assessment system based on electroencephalography (EEG) and differential language large m...

Seizure Sources Can Be Imaged from Scalp EEG by Means of Biophysically Constrained Deep Neural Networks.

Seizure localization is important for managing drug-resistant focal epilepsy. Here, the capability of a novel deep learning-based source imaging frame...

Dual Attention Relation Network With Fine-Tuning for Few-Shot EEG Motor Imagery Classification.

Recently, motor imagery (MI) electroencephalography (EEG) classification techniques using deep learning have shown improved performance over conventio...

CNN-Informer: A hybrid deep learning model for seizure detection on long-term EEG.

Timely detecting epileptic seizures can significantly reduce accidental injuries of epilepsy patients and offer a novel intervention approach to impro...

Deep Autoencoder for Real-Time Single-Channel EEG Cleaning and Its Smartphone Implementation Using TensorFlow Lite With Hardware/Software Acceleration.

OBJECTIVE: To remove signal contamination in electroencephalogram (EEG) traces coming from ocular, motion, and muscular artifacts which degrade signal...

Machine learning localization to identify the epileptogenic side in mesial temporal lobe epilepsy.

BACKGROUND: Mesial temporal sclerosis (MTS) is the most common pathology associated with drug-resistant mesial temporal lobe epilepsy (mTLE) in adults...

Attention Induced Dual Convolutional-Capsule Network (AIDC-CN): A deep learning framework for motor imagery classification.

In recent times, Electroencephalography (EEG)-based motor imagery (MI) decoding has garnered significant attention due to its extensive applicability ...

An interpretable and generalizable deep learning model for iEEG-based seizure prediction using prototype learning and contrastive learning.

Epileptic seizure prediction plays a crucial role in enhancing the quality of life for individuals with epilepsy. Over recent years, a multitude of de...

Semiology Extraction and Machine Learning-Based Classification of Electronic Health Records for Patients With Epilepsy: Retrospective Analysis.

BACKGROUND: Obtaining and describing semiology efficiently and classifying seizure types correctly are crucial for the diagnosis and treatment of epil...

Real-time sub-milliwatt epilepsy detection implemented on a spiking neural network edge inference processor.

Analyzing electroencephalogram (EEG) signals to detect the epileptic seizure status of a subject presents a challenge to existing technologies aimed a...

Graph neural networks for electroencephalogram analysis: Alzheimer's disease and epilepsy use cases.

Electroencephalography (EEG) is widely used as a non-invasive technique for the diagnosis of several brain disorders, including Alzheimer's disease an...

Adapting Action Recognition Neural Networks for Automated Infantile Spasm Detection.

Infantile spasms are a severe epileptic syndrome characterized by short muscular contractions lasting from 0.5 to 2 seconds. They are often misdiagnos...

Deep Neural Network-Based Empirical Mode Decomposition for Motor Imagery EEG Classification.

Motor imagery refers to the brain's response during the mental simulation of physical activities, which can be detected through electroencephalogram (...

A Lightweight Convolutional Neural Network-Reformer Model for Efficient Epileptic Seizure Detection.

A real-time and reliable automatic detection system for epileptic seizures holds significant value in assisting physicians with rapid diagnosis and tr...

MMF-NNs: Multi-modal Multi-granularity Fusion Neural Networks for brain networks and its application to epilepsy identification.

Structural and functional brain networks are generated from two scan sequences of magnetic resonance imaging data, which can provide different perspec...

Machine-learning-based classification of obstructive sleep apnea using 19-channel sleep EEG data.

OBJECTIVE: This study aimed to investigate the neurophysiological effects of obstructive sleep apnea (OSA) using multi-channel sleep electroencephalog...