Seizures

Dual-Modal Information Bottleneck Network for Seizure Detection.

In recent years, deep learning has shown very competitive performance in seizure detection. However, most of the currently used methods either convert electroencephalogram (EEG) signals into spectral images and employ 2D-CNNs, or split the one-dimensional (1D) features of EEG signals into many segments and employ 1D-CNNs. Moreover, these investigations are further constrained by the absence of con...

An AI-Inspired Spatio-Temporal Neural Network for EEG-Based Emotional Status.

The accurate identification of the human emotional status is crucial for an efficient human-robot interaction (HRI). As such, we have witnessed extensive research efforts made in developing robust and accurate brain-computer interfacing models based on diverse biosignals. In particular, previous research has shown that an Electroencephalogram (EEG) can provide deep insight into the state of emotio...

A novel ANN adaptive Riemannian-based kernel classification for motor imagery.

More recently, a number of studies show the interest of the use of the Riemannian geometry in EEG classification. The idea is to exploit the EEG covar...

Emotion Detection Using Deep Normalized Attention-Based Neural Network and Modified-Random Forest.

In the contemporary world, emotion detection of humans is procuring huge scope in extensive dimensions such as bio-metric security, HCI (human-compute...

An efficient deep learning framework for P300 evoked related potential detection in EEG signal.

BACKGROUND: Incorporating the time-frequency localization properties of Gabor transform (GT), the complexity understandings of convolutional neural ne...

Hybrid fuzzy deep neural network toward temporal-spatial-frequency features learning of motor imagery signals.

Achieving an efficient and reliable method is essential to interpret a user's brain wave and deliver an accurate response in biomedical signal process...

Classification at the accuracy limit: facing the problem of data ambiguity.

Data classification, the process of analyzing data and organizing it into categories or clusters, is a fundamental computing task of natural and artif...

Transforming epilepsy research: A systematic review on natural language processing applications.

Despite improved ancillary investigations in epilepsy care, patients' narratives remain indispensable for diagnosing and treatment monitoring. This we...

Fast Sleep Stage Classification Using Cascaded Support Vector Machines with Single-Channel EEG Signals.

Long-term sleep stage monitoring is very important for the diagnosis and treatment of insomnia. With the development of wearable electroencephalogram ...

Computer-aided diagnosis of autism spectrum disorder from EEG signals using deep learning with FAWT and multiscale permutation entropy features.

Autism spectrum disorder (ASD), a neurodevelopment disorder, is characterized by significant difficulties in social interaction and emerges as a major...

An Efficient AP-ANN-Based Multimethod Fusion Model to Detect Stress through EEG Signal Analysis.

Stress is a universal emotion that every human experiences daily. Psychologists say stress may lead to heart attack, depression, hypertension, strokes...

Teleoperation control of a wheeled mobile robot based on Brain-machine Interface.

This paper presents a novel teleoperation system using Electroencephalogram (EEG) to control the motion of a wheeled mobile robot (WMR). Different fro...

Characterizing physiological high-frequency oscillations using deep learning.

Intracranially-recorded interictal high-frequency oscillations (HFOs) have been proposed as a promising spatial biomarker of the epileptogenic zone. H...

A Transferable Deep Learning Prognosis Model for Predicting Stroke Patients' Recovery in Different Rehabilitation Trainings.

Since the underlying mechanisms of neurorehabilitation are not fully understood, the prognosis of stroke recovery faces significant difficulties. Reco...

A comparison between robot-guided and stereotactic frame-based stereoelectroencephalography (SEEG) electrode implantation for drug-resistant epilepsy.

The original stereoelectroencephalography frame-based implantation technique has been proven to be safe and effective. But this procedure is complicat...

Epileptic Seizure Detection Using a Hybrid 1D CNN-Machine Learning Approach from EEG Data.

Electroencephalography (EEG) is a widely used technique for the detection of epileptic seizures. It can be recorded in a noninvasive manner to present...

Automatic seizure detection by convolutional neural networks with computational complexity analysis.

BACKGROUND AND OBJECTIVES: Nowadays, an automated computer-aided diagnosis (CAD) is an approach that plays an important role in the detection of healt...

Sleep Staging Framework with Physiologically Harmonized Sub-Networks.

Sleep screening is an important tool for both healthcare and neuroscientific research. Automatic sleep scoring is an alternative to the time-consuming...

Staging study of single-channel sleep EEG signals based on data augmentation.

INTRODUCTION: Accurate sleep staging is an essential basis for sleep quality assessment and plays an important role in sleep quality research. However...

Real-time noise cancellation with deep learning.

Biological measurements are often contaminated with large amounts of non-stationary noise which require effective noise reduction techniques. We prese...