Neurology

Seizures

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

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In Silico drug repurposing pipeline using deep learning and structure based approaches in epilepsy.

Due to considerable global prevalence and high recurrence rate, the pursuit of effective new medicat...

A machine learning artefact detection method for single-channel infant event-related potential studies.

. Automated detection of artefact in stimulus-evoked electroencephalographic (EEG) data recorded in ...

Identification and diagnosis of schizophrenia based on multichannel EEG and CNN deep learning model.

This paper proposes a high-accuracy EEG-based schizophrenia (SZ) detection approach. Unlike comparab...

Seizure Detection of EEG Signals Based on Multi-Channel Long- and Short-Term Memory-Like Spiking Neural Model.

Seizure is a common neurological disorder that usually manifests itself in recurring seizure, and th...

Multi-Modal Electrophysiological Source Imaging With Attention Neural Networks Based on Deep Fusion of EEG and MEG.

The process of reconstructing underlying cortical and subcortical electrical activities from Electro...

LGGNet: Learning From Local-Global-Graph Representations for Brain-Computer Interface.

Neuropsychological studies suggest that co-operative activities among different brain functional are...

An Identification Method for Road Hypnosis Based on Human EEG Data.

The driver in road hypnosis has not only some external characteristics, but also some internal chara...

Automatic diagnosis of epileptic seizures using entropy-based features and multimodel deep learning approaches.

Epilepsy is one of the most common brain diseases, characterised by repeated seizures that occur on ...

An auto-segmented multi-time window dual-scale neural network for brain-computer interfaces based on event-related potentials.

Event-related potentials (ERPs) are cerebral responses to cognitive processes, also referred to as c...

Temporal-spatial cross attention network for recognizing imagined characters.

Previous research has primarily employed deep learning models such as Convolutional Neural Networks ...

EEG-based motor imagery channel selection and classification using hybrid optimization and two-tier deep learning.

Brain-computer interface (BCI) technology holds promise for individuals with profound motor impairme...

Deep learning-based stress detection for daily life use using single-channel EEG and GSR in a virtual reality interview paradigm.

This research aims to establish a practical stress detection framework by integrating physiological ...

A Method to Extract Task-Related EEG Feature Based on Lightweight Convolutional Neural Network.

Unlocking task-related EEG spectra is crucial for neuroscience. Traditional convolutional neural net...

Unraveling Brain Synchronisation Dynamics by Explainable Neural Networks using EEG Signals: Application to Dyslexia Diagnosis.

The electrical activity of the neural processes involved in cognitive functions is captured in EEG s...

Power spectral density-based resting-state EEG classification of first-episode psychosis.

Historically, the analysis of stimulus-dependent time-frequency patterns has been the cornerstone of...

A Deep Learning Approach to Estimate Multi-Level Mental Stress From EEG Using Serious Games.

Stress is revealed by the inability of individuals to cope with their environment, which is frequent...

MASA-TCN: Multi-Anchor Space-Aware Temporal Convolutional Neural Networks for Continuous and Discrete EEG Emotion Recognition.

Emotion recognition from electroencephalogram (EEG) signals is a critical domain in biomedical resea...

SMARTSeiz: Deep Learning With Attention Mechanism for Accurate Seizure Recognition in IoT Healthcare Devices.

The Internet of Things (IoT) is capable of controlling the healthcare monitoring system for remote-b...

Efficient Generalized Electroencephalography-Based Drowsiness Detection Approach with Minimal Electrodes.

Drowsiness is a main factor for various costly defects, even fatal accidents in areas such as constr...

MSE-VGG: A Novel Deep Learning Approach Based on EEG for Rapid Ischemic Stroke Detection.

Ischemic stroke is a type of brain dysfunction caused by pathological changes in the blood vessels o...

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