Neurology

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

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DSFE: Decoding EEG-Based Finger Motor Imagery Using Feature-Dependent Frequency, Feature Fusion and Ensemble Learning.

Accurate decoding finger motor imagery is essential for fine motor control using EEG signals. Howeve...

DCNet: A Self-Supervised EEG Classification Framework for Improving Cognitive Computing-Enabled Smart Healthcare.

Cognitive computing endeavors to construct models that emulate brain functions, which can be explore...

Iteratively Calibratable Network for Reliable EEG-Based Robotic Arm Control.

Robotic arms are increasingly being utilized in shared workspaces, which necessitates the accurate i...

Estimating highest capacity propulsion performance using backward-directed force during walking evaluation for individuals with acquired brain injury.

There are over 5.3 million Americans who face acquired brain injury (ABI)-related disability as well...

SR-TWAS: leveraging multiple reference panels to improve transcriptome-wide association study power by ensemble machine learning.

Multiple reference panels of a given tissue or multiple tissues often exist, and multiple regression...

Machine learning of brain-specific biomarkers from EEG.

BACKGROUND: Electroencephalography (EEG) has a long history as a clinical tool to study brain functi...

Effective Emotion Recognition by Learning Discriminative Graph Topologies in EEG Brain Networks.

Multichannel electroencephalogram (EEG) is an array signal that represents brain neural networks and...

A data augmentation procedure to improve detection of spike ripples in brain voltage recordings.

Epilepsy is a major neurological disorder characterized by recurrent, spontaneous seizures. For pati...

Machine learning characterization of a rare neurologic disease via electronic health records: a proof-of-principle study on stiff person syndrome.

BACKGROUND: Despite the frequent diagnostic delays of rare neurologic diseases (RND), it remains dif...

Comparison of three artificial intelligence algorithms for automatic cobb angle measurement using teaching data specific to three disease groups.

Spinal deformities, including adolescent idiopathic scoliosis (AIS) and adult spinal deformity (ASD)...

Combining robotics and functional electrical stimulation for assist-as-needed support of leg movements in stroke patients: A feasibility study.

PURPOSE: Rehabilitation technology can be used to provide intensive training in the early phases aft...

Machine learning-driven diagnosis of multiple sclerosis from whole blood transcriptomics.

Multiple sclerosis (MS) is a neurological disorder characterized by immune dysregulation. It begins ...

Data-driven prediction of spinal cord injury recovery: An exploration of current status and future perspectives.

Spinal Cord Injury (SCI) presents a significant challenge in rehabilitation medicine, with recovery ...

A novel graph neural network method for Alzheimer's disease classification.

Alzheimer's disease (AD) is a chronic neurodegenerative disease. Early diagnosis are very important ...

Botulinum Toxin Type A (BoNT-A) Use for Post-Stroke Spasticity: A Multicenter Study Using Natural Language Processing and Machine Learning.

We conducted a multicenter and retrospective study to describe the use of botulinum toxin type A (Bo...

The stroke outcome optimization project: Acute ischemic strokes from a comprehensive stroke center.

Stroke is a leading cause of disability, and Magnetic Resonance Imaging (MRI) is routinely acquired ...

From data to decisions: AI and functional connectivity for diagnosis, prognosis, and recovery prediction in stroke.

Stroke is a severe medical condition which may lead to permanent disability conditions. The initial ...

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