Neurology

Discriminating and understanding brain states in children with epileptic spasms using deep learning and graph metrics analysis of brain connectivity.

BACKGROUND AND OBJECTIVE: Epilepsy is a brain disorder consisting of abnormal electrical discharges ...

Anesthetic implications in the placement of deep brain electrodes through a stereotactic robot in the study of drug resistant epilepsy.

Automated in-depth cerebral arterial labelling using cerebrovascular vasculature reframing and deep neural networks.

Identifying the cerebral arterial branches is essential for undertaking a computational approach to ...

Spintronic leaky-integrate-fire spiking neurons with self-reset and winner-takes-all for neuromorphic computing.

Neuromorphic computing using nonvolatile memories is expected to tackle the memory wall and energy e...

Spinal disease diagnosis assistant based on MRI images using deep transfer learning methods.

INTRODUCTION: In light of the potential problems of missed diagnosis and misdiagnosis in the diagnos...

A 3-DoF robotic platform for the rehabilitation and assessment of reaction time and balance skills of MS patients.

The central nervous system (CNS) exploits anticipatory (APAs) and compensatory (CPAs) postural adjus...

Multi-band spatial feature extraction and classification for motor imaging EEG signals based on OSFBCSP-GAO-SVM model : EEG signal processing.

Electroencephalogram (EEG) is a non-stationary random signal with strong background noise, which mak...

Imaging Advances in Stroke: Use of Advanced Neurovascular Imaging or Disruptive Innovation With Artificial Intelligence?

Cross-Domain Transfer of EEG to EEG or ECG Learning for CNN Classification Models.

Electroencephalography (EEG) is often used to evaluate several types of neurological brain disorders...

Abstract representations emerge naturally in neural networks trained to perform multiple tasks.

Humans and other animals demonstrate a remarkable ability to generalize knowledge across distinct co...

Deep learning for the diagnosis of mesial temporal lobe epilepsy.

OBJECTIVE: This study aimed to enable the automatic detection of the hippocampus and diagnose mesial...

Artificial Contrast: Deep Learning for Reducing Gadolinium-Based Contrast Agents in Neuroradiology.

Deep learning approaches are playing an ever-increasing role throughout diagnostic medicine, especia...

Neural stochastic differential equations network as uncertainty quantification method for EEG source localization.

EEG source localization remains a challenging problem given the uncertain conductivity values of the...

Multiparametric MRI: From Simultaneous Rapid Acquisition Methods and Analysis Techniques Using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics.

With the recent advancements in rapid imaging methods, higher numbers of contrasts and quantitative ...

Combining robot-assisted therapy with virtual reality or using it alone? A systematic review on health-related quality of life in neurological patients.

BACKGROUND: In the field of neurorehabilitation, robot-assisted therapy (RAT) and virtual reality (V...

siVAE: interpretable deep generative models for single-cell transcriptomes.

Neural networks such as variational autoencoders (VAE) perform dimensionality reduction for the visu...

Ethical issues when using digital biomarkers and artificial intelligence for the early detection of dementia.

Dementia poses a growing challenge for health services but remains stigmatized and under-recognized....

Artificial Intelligence-Based Voice Assessment of Patients with Parkinson's Disease Off and On Treatment: Machine vs. Deep-Learning Comparison.

Parkinson's Disease (PD) is one of the most common non-curable neurodegenerative diseases. Diagnosis...

An attention-based deep learning approach for the classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG.

. This study aims to design and implement the first deep learning (DL) model to classify subjects in...

A neurodynamic approach for nonsmooth optimal power consumption of intelligent and connected vehicles.

This paper investigates a class of power consumption minimization and equalization for intelligent a...

A low-power and flexible bioinspired artificial sensory neuron capable of tactile perceptual and associative learning.

Biomimetic haptic neuron systems have received a lot of attention from the booming artificial intell...