Neurology

Seizures

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

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An extended clinical EEG dataset with 15,300 automatically labelled recordings for pathology decoding.

Automated clinical EEG analysis using machine learning (ML) methods is a growing EEG research area. ...

Effect of Lower Limb Exoskeleton on the Modulation of Neural Activity and Gait Classification.

Neurorehabilitation with robotic devices requires a paradigm shift to enhance human-robot interactio...

Sex-related patterns in the electroencephalogram and their relevance in machine learning classifiers.

Deep learning is increasingly being proposed for detecting neurological and psychiatric diseases fro...

Analyzing of optimal classifier selection for EEG signals of depression patients based on intelligent fuzzy decision support systems.

Electroencephalograms (EEG) is used to assess patients' clinical records of depression (EEG). The di...

A 0.99-to-4.38 uJ/class Event-Driven Hybrid Neural Network Processor for Full-Spectrum Neural Signal Analyses.

Versatile and energy-efficient neural signal processors are in high demand in brain-machine interfac...

Human-robot collaborative task planning using anticipatory brain responses.

Human-robot interaction (HRI) describes scenarios in which both human and robot work as partners, sh...

Deep learning in neuroimaging of epilepsy.

In recent years, artificial intelligence, particularly deep learning (DL), has demonstrated utility ...

Effect of sugammadex on processed EEG parameters in patients undergoing robot-assisted radical prostatectomy.

BACKGROUND: Sugammadex has been associated with increases in the bispectral index (BIS). We evaluate...

Perturbing BEAMs: EEG adversarial attack to deep learning models for epilepsy diagnosing.

Deep learning models have been widely used in electroencephalogram (EEG) analysis and obtained excel...

Characteristic analysis of epileptic brain network based on attention mechanism.

Constructing an efficient and accurate epilepsy detection system is an urgent research task. In this...

Deep Learning Models for Stress Analysis in University Students: A Sudoku-Based Study.

Due to the phenomenon of "involution" in China, the current generation of college and university stu...

EEG motor imagery classification using deep learning approaches in naïve BCI users.

Motor Imagery (MI)-Brain Computer-Interfaces (BCI) illiteracy defines that not all subjects can achi...

A neuromorphic physiological signal processing system based on VO memristor for next-generation human-machine interface.

Physiological signal processing plays a key role in next-generation human-machine interfaces as phys...

FCAN-XGBoost: A Novel Hybrid Model for EEG Emotion Recognition.

In recent years, artificial intelligence (AI) technology has promoted the development of electroence...

Genetic algorithm designed for optimization of neural network architectures for intracranial EEG recordings analysis.

The current practices of designing neural networks rely heavily on subjective judgment and heuristic...

Source Aware Deep Learning Framework for Hand Kinematic Reconstruction Using EEG Signal.

The ability to reconstruct the kinematic parameters of hand movement using noninvasive electroenceph...

A Product Fuzzy Convolutional Network for Detecting Driving Fatigue.

Existing driving fatigue detection methods rarely consider how to effectively fuse the advantages of...

The clinical application of neuro-robot in the resection of epileptic foci: a novel method assisting epilepsy surgery.

During surgery for foci-related epilepsy, neurosurgeons face significant difficulties in identifying...

Detection of ADHD from EEG signals using new hybrid decomposition and deep learning techniques.

Attention deficit hyperactivity disorder (ADHD) is considered one of the most common psychiatric dis...

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