Neurology

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

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Neuromorphic engineering: Artificial brains for artificial intelligence.

Neuromorphic engineering is a research discipline that tries to bridge the gaps between neuroscience...

Wearable EEG Neurofeedback Based-on Machine Learning Algorithms for Children with Autism: A Randomized, Placebo-controlled Study.

OBJECTIVE: Behavioral interventions have been shown to ameliorate the electroencephalogram (EEG) dyn...

A continuous pursuit dataset for online deep learning-based EEG brain-computer interface.

This dataset is from an EEG brain-computer interface (BCI) study investigating the use of deep learn...

Automatic detection and proximity quantification of inferior alveolar nerve and mandibular third molar on cone-beam computed tomography.

OBJECTIVES: During mandibular third molar (MTM) extraction surgery, preoperative analysis to quantif...

Classification of hand movements from EEG using a FusionNet based LSTM network.

. Accurate classification of electroencephalogram (EEG) signals is crucial for advancing brain-compu...

Metrics for evaluation of automatic epileptogenic zone localization in intracranial electrophysiology.

INTRODUCTION: Precise localization of the epileptogenic zone is critical for successful epilepsy sur...

Enhancing Motor Imagery Classification with Residual Graph Convolutional Networks and Multi-Feature Fusion.

Stroke, an abrupt cerebrovascular ailment resulting in brain tissue damage, has prompted the adoptio...

Deep Learning Recognition of Paroxysmal Kinesigenic Dyskinesia Based on EEG Functional Connectivity.

Paroxysmal kinesigenic dyskinesia (PKD) is a rare neurological disorder marked by transient involunt...

A Parkinson's disease-related nuclei segmentation network based on CNN-Transformer interleaved encoder with feature fusion.

Automatic segmentation of Parkinson's disease (PD) related deep gray matter (DGM) nuclei based on br...

Identification of movie encoding neurons enables movie recognition AI.

Natural visual scenes are dominated by spatiotemporal image dynamics, but how the visual system inte...

A Novel and Powerful Dual-Stream Multi-Level Graph Convolution Network for Emotion Recognition.

Emotion recognition enables machines to more acutely perceive and understand users' emotional states...

A Transcriptomics-Based Machine Learning Model Discriminating Mild Cognitive Impairment and the Prediction of Conversion to Alzheimer's Disease.

The clinical spectrum of Alzheimer's disease (AD) ranges dynamically from asymptomatic and mild cogn...

Machine learning for stroke in heart failure with reduced ejection fraction but without atrial fibrillation: A post-hoc analysis of the WARCEF trial.

BACKGROUND: The prediction of ischaemic stroke in patients with heart failure with reduced ejection ...

Emerging Frontiers in Conformational Exploration of Disordered Proteins: Integrating Autoencoder and Molecular Simulations.

Intrinsically disordered proteins (IDPs) are closely associated with a number of neurodegenerative d...

Optimizing early diagnosis by integrating multiple classifiers for predicting brain stroke and critical diseases.

Machine learning has gained attention in the medical field. Continuous efforts are being made to dev...

A digital neuromorphic system for working memory based on spiking neuron-astrocyte network.

Among various types of memory, working memory (WM) plays a crucial role in reasoning, decision-makin...

Multi-compartment neuron and population encoding powered spiking neural network for deep distributional reinforcement learning.

Inspired by the brain's information processing using binary spikes, spiking neural networks (SNNs) o...

An interpretable machine learning scoring tool for estimating time to recurrence readmissions in stroke patients.

BACKGROUND: Stroke recurrence readmission poses an additional burden on both patients and healthcare...

Assessing operator stress in collaborative robotics: A multimodal approach.

In the era of Industry 4.0, the study of Human-Robot Collaboration (HRC) in advancing modern manufac...

An intelligent magnetic resonance imagining-based multistage Alzheimer's disease classification using swish-convolutional neural networks.

Alzheimer's disease (AD) refers to a neurological disorder that causes damage to brain cells and res...

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