Neurology

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

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Multiclass Classification Framework of Motor Imagery EEG by Riemannian Geometry Networks.

In motor imagery (MI) tasks for brain computer interfaces (BCIs), the spatial covariance matrix (SCM...

Incremental Classification for High-Dimensional EEG Manifold Representation Using Bidirectional Dimensionality Reduction and Prototype Learning.

In brain-computer interface (BCI) systems, symmetric positive definite (SPD) manifold within Riemann...

EEG Temporal-Spatial Feature Learning for Automated Selection of Stimulus Parameters in Electroconvulsive Therapy.

The risk of adverse effects in Electroconvulsive Therapy (ECT), such as cognitive impairment, can be...

Unlocking Dreams and Dreamless Sleep: Machine Learning Classification With Optimal EEG Channels.

Research suggests that dreams play a role in the regulation of emotional processing and memory conso...

Analysis and validation of programmed cell death genes associated with spinal cord injury progression based on bioinformatics and machine learning.

BACKGROUND: Spinal cord injury (SCI) is a severe condition affecting the central nervous system. It ...

Alzheimer's disease classification using hybrid loss Psi-Net segmentation and a new hybrid network model.

Alzheimer's disease (AD) is a type of brain disorder that is becoming more prevalent worldwide. It i...

Geometric deep learning with adaptive full-band spatial diffusion for accurate, efficient, and robust cortical parcellation.

Cortical parcellation delineates the cerebral cortex into distinct regions according to their distin...

EEGConvNeXt: A novel convolutional neural network model for automated detection of Alzheimer's Disease and Frontotemporal Dementia using EEG signals.

BACKGROUND AND OBJECTIVE: Deep learning models have gained widespread adoption in healthcare for acc...

Analyzing patterns of frequent mental distress in Alzheimer's patients: A generative AI approach.

This study tackles creating Python code for beginners with generative AI and analyzing trends in men...

Patient performance assessment methods for upper extremity rehabilitation in assist-as-needed therapy strategies: a comprehensive review.

This paper aims to comprehensively review patient performance assessment (PPA) methods used in assis...

Characterizing drivers of change in intraoperative cerebral saturation using supervised machine learning.

Regional cerebral oxygen saturation (rSO) is used to monitor cerebral perfusion with emerging eviden...

Machine learning based finite element analysis for personalized prediction of pressure injury risk in patients with spinal cord injury.

BACKGROUND AND OBJECTIVE: Patients with spinal cord injury (SCI), are prone to pressure injury (PI) ...

Enhanced EEG-based cognitive workload detection using RADWT and machine learning.

Understanding cognitive workload improves learning performance and provides insights into human cogn...

Can muscle synergies shed light on the mechanisms underlying motor gains in response to robot-assisted gait training in children with cerebral palsy?

BACKGROUND: Children with cerebral palsy (CP) often experience gait impairments. Robot-assisted gait...

Improving stroke risk prediction by integrating XGBoost, optimized principal component analysis, and explainable artificial intelligence.

The relevance of the study is due to the growing number of diseases of the cerebrovascular system, i...

ECA-FusionNet: a hybrid EEG-fNIRS signals network for MI classification.

. Among all BCI paradigms, motion imagery (MI) has gained favor among researchers because it allows ...

A low-cost transhumeral prosthesis operated via an ML-assisted EEG-head gesture control system.

Key challenges in upper limb prosthetics include a lack of effective control systems, the often inva...

Predicting cognitive decline: Deep-learning reveals subtle brain changes in pre-MCI stage.

BACKGROUND: Mild cognitive impairment (MCI) and preclinical MCI (e.g., subjective cognitive decline,...

DeepPrep: an accelerated, scalable and robust pipeline for neuroimaging preprocessing empowered by deep learning.

Neuroimaging has entered the era of big data. However, the advancement of preprocessing pipelines fa...

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