Neurology

Self-supervised motor imagery EEG recognition model based on 1-D MTCNN-LSTM network.

Aiming for the research on the brain-computer interface (BCI), it is crucial to design a MI-EEG reco...

Can Artificial Intelligence Mitigate Missed Diagnoses by Generating Differential Diagnoses for Neurosurgeons?

BACKGROUND/OBJECTIVE: Neurosurgery emphasizes the criticality of accurate differential diagnoses, wi...

Artificial intelligence: Can it help us better grasp the idea of epilepsy? An exploratory dialogue with ChatGPT and DALL·E 2.

BACKGROUND: The conceptual definition of epilepsy has been changing over decades and remains debatab...

Optimal Channel Selection of Multiclass Motor Imagery Classification Based on Fusion Convolutional Neural Network with Attention Blocks.

The widely adopted paradigm in brain-computer interfaces (BCIs) involves motor imagery (MI), enablin...

Machine learning for the detection and diagnosis of cognitive impairment in Parkinson's Disease: A systematic review.

BACKGROUND: Parkinson's Disease is the second most common neurological disease in over 60s. Cognitiv...

Risk Factors for Perinatal Arterial Ischemic Stroke: A Machine Learning Approach.

BACKGROUND AND OBJECTIVES: Perinatal arterial ischemic stroke (PAIS) is a focal vascular brain injur...

Effects of Rehabilitation Robot Training on Physical Function, Functional Recovery, and Daily Living Activities in Patients with Sub-Acute Stroke.

Stroke often results in sensory deficits, muscular weakness, and diminished postural control, thereb...

CKG-IMC: An inductive matrix completion method enhanced by CKG and GNN for Alzheimer's disease compound-protein interactions prediction.

Alzheimer's disease (AD) is one of the most prevalent chronic neurodegenerative disorders globally, ...

Effects of robot-assisted gait training using the Welwalk on gait independence for individuals with hemiparetic stroke: an assessor-blinded, multicenter randomized controlled trial.

BACKGROUND: Gait disorder remains a major challenge for individuals with stroke, affecting their qua...

Effect of task-oriented training assisted by force feedback hand rehabilitation robot on finger grasping function in stroke patients with hemiplegia: a randomised controlled trial.

BACKGROUND: Over 80% of patients with stroke experience finger grasping dysfunction, affecting indep...

Identifying potential (re)hemorrhage among sporadic cerebral cavernous malformations using machine learning.

The (re)hemorrhage in patients with sporadic cerebral cavernous malformations (CCM) was the primary ...

Multi-scale 3D-CRU for EEG emotion recognition.

In this paper, we propose a novel multi-scale 3D-CRU model, with the goal of extracting more discrim...

The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach.

Epilepsy surgery is effective for patients with medication-resistant seizures, however 20-40% of the...

Developing machine learning models to predict multi-class functional outcomes and death three months after stroke in Sweden.

Globally, stroke is the third-leading cause of mortality and disability combined, and one of the cos...

The diagnostic performance of AI-based algorithms to discriminate between NMOSD and MS using MRI features: A systematic review and meta-analysis.

BACKGROUND: Magnetic resonance imaging [MRI] findings in Neuromyelitis optica spectrum disorder [NMO...

Making robots matter in dementia care: Conceptualising the triadic interaction between caregiver, resident and robot animal.

While previous research studies have focused on either caregivers' or residents' perception and use ...

Voxel level dense prediction of acute stroke territory in DWI using deep learning segmentation models and image enhancement strategies.

PURPOSE: To build a stroke territory classifier model in DWI by designing the problem as a multiclas...

Dementia and AI.

Hspb1 and Lgals3 in spinal neurons are closely associated with autophagy following excitotoxicity based on machine learning algorithms.

Excitotoxicity represents the primary cause of neuronal death following spinal cord injury (SCI). Wh...

Multiclass motor imagery classification with Riemannian geometry and temporal-spectral selection.

Motor imagery (MI) based brain-computer interfaces (BCIs) decode the users' intentions from electroe...

Using machine learning to discover traumatic brain injury patient phenotypes: national concussion surveillance system Pilot.

OBJECTIVE: The objective is to determine whether unsupervised machine learning identifies traumatic ...