Neurology

Seizures

Latest AI and machine learning research in seizures for healthcare professionals.

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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. Howeve...

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...

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

BACKGROUND AND OBJECTIVE: Emotion classification tasks based on electroencephalography (EEG) are an ...

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 hea...

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 in...

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 d...

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 experie...

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 a...

Dual-Modal Information Bottleneck Network for Seizure Detection.

In recent years, deep learning has shown very competitive performance in seizure detection. However,...

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 in...

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 cl...

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 dimensio...

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 co...

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...

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...

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

Despite improved ancillary investigations in epilepsy care, patients' narratives remain indispensabl...

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...

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 difficu...

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 ...

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 mot...

Characterizing physiological high-frequency oscillations using deep learning.

Intracranially-recorded interictal high-frequency oscillations (HFOs) have been proposed as a promis...

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