Neurology

A fine-tuning deep residual convolutional neural network for emotion recognition based on frequency-channel matrices representation of one-dimensional electroencephalography.

Emotion recognition (ER) plays a crucial role in enabling machines to perceive human emotional and p...

ADHD/CD-NET: automated EEG-based characterization of ADHD and CD using explainable deep neural network technique.

UNLABELLED: In this study, attention deficit hyperactivity disorder (ADHD), a childhood neurodevelop...

Machine Learning Web Application for Predicting Functional Outcomes in Patients With Traumatic Spinal Cord Injury Following Inpatient Rehabilitation.

Accurately predicting functional outcomes in patients with spinal cord injury (SCI) helps clinicians...

Label-free identification of protein aggregates using deep learning.

Protein misfolding and aggregation play central roles in the pathogenesis of various neurodegenerati...

Editorial: Network science approaches to risk assessment of mental disorders and dementia.

Combining Feature Selection Techniques and Neurofuzzy Systems for the Prediction of Total Viable Counts in Beef Fillets Using Multispectral Imaging.

In the food industry, quality and safety issues are associated with consumers' health condition. The...

Improving Neurology Clinical Care With Natural Language Processing Tools.

The integration of natural language processing (NLP) tools into neurology workflows has the potentia...

Editorial: Technologies to support elderly patients with dementia.

Neuroimaging, wearable sensors, and blood-based biomarkers reveal hyperacute changes in the brain after sub-concussive impacts.

Impacts in mixed martial arts (MMA) have been studied mainly in regard to the long-term effects of c...

Machine-learning assisted swallowing assessment: a deep learning-based quality improvement tool to screen for post-stroke dysphagia.

INTRODUCTION: Post-stroke dysphagia is common and associated with significant morbidity and mortalit...

Study on brain damage patterns of COVID-19 patients based on EEG signals.

OBJECTIVE: The coronavirus disease 2019 (COVID-19) is an acute respiratory infectious disease caused...

Cerebrospinal fluid flow artifact reduction with deep learning to optimize the evaluation of spinal canal stenosis on spine MRI.

PURPOSE: The aim of study was to employ the Cycle Generative Adversarial Network (CycleGAN) deep lea...

Assessing the performance of ChatGPT's responses to questions related to epilepsy: A cross-sectional study on natural language processing and medical information retrieval.

BACKGROUND: Epilepsy is a neurological condition marked by frequent seizures and various cognitive a...

An Effective Hybrid Deep Learning Model for Single-Channel EEG-Based Subject-Independent Drowsiness Recognition.

Nowadays, road accidents pose a severe risk in cases of sleep disorders. We proposed a novel hybrid ...

Accuracy of ChatGPT generated diagnosis from patient's medical history and imaging findings in neuroradiology cases.

PURPOSE: The noteworthy performance of Chat Generative Pre-trained Transformer (ChatGPT), an artific...

Salient Arithmetic Data Extraction from Brain Activity via an Improved Deep Network.

Interpretation of neural activity in response to stimulations received from the surrounding environm...

Artificial intelligence (AI) for neurologists: do digital neurones dream of electric sheep?

Artificial intelligence (AI) is routinely mentioned in journals and newspapers, and non-technical ou...

Combining brain-computer interfaces with deep reinforcement learning for robot training: a feasibility study in a simulation environment.

Deep reinforcement learning (RL) is used as a strategy to teach robot agents how to autonomously lea...

Development and internal validation of machine learning-based models and external validation of existing risk scores for outcome prediction in patients with ischaemic stroke.

AIMS: We developed new machine learning (ML) models and externally validated existing statistical mo...

Feasibility interventional study investigating PAIN in neurorehabilitation through wearabLE SensorS (PAINLESS): a study protocol.

INTRODUCTION: Millions of people survive injuries to the central or peripheral nervous system for wh...

Predictive Value of Acute Neurological Progression Using Bayesian CT Perfusion for Acute Ischemic Stroke with Large or Median Vessel Occlusion.

OBJECTIVE: Since the efficacy of mechanical thrombectomy (MT) for acute cerebral infarction due to l...