Neurology

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

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Deep Learning for Enhanced Prosthetic Control: Real-Time Motor Intent Decoding for Simultaneous Control of Artificial Limbs.

The development of advanced prosthetic devices that can be seamlessly used during an individual's da...

Socially Assistive Robots for patients with Alzheimer's Disease: A scoping review.

BACKGROUND: The most common form of dementia, Alzheimer's Disease (AD), is challenging for both thos...

Identification of clinical disease trajectories in neurodegenerative disorders with natural language processing.

Neurodegenerative disorders exhibit considerable clinical heterogeneity and are frequently misdiagno...

Facilitators and barriers to codesigning social robots with older adults with dementia: a scoping review protocol.

INTRODUCTION: Social robots including telepresence robots have emerged as potential support in demen...

Time series classification of multi-channel nerve cuff recordings using deep learning.

Neurostimulation and neural recording are crucial to develop neuroprostheses that can restore functi...

EMG-based Multi-User Hand Gesture Classification via Unsupervised Transfer Learning Using Unknown Calibration Gestures.

The poor generalization performance and heavy training burden of the gesture classification model co...

Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications.

Human-machine interaction (HMI) technology has undergone significant advancements in recent years, e...

Presynaptic Dopaminergic Imaging Characterizes Patients with REM Sleep Behavior Disorder Due to Synucleinopathy.

OBJECTIVE: To apply a machine learning analysis to clinical and presynaptic dopaminergic imaging dat...

An accessible deep learning tool for voxel-wise classification of brain malignancies from perfusion MRI.

Noninvasive differential diagnosis of brain tumors is currently based on the assessment of magnetic ...

Predicting long-term progression of Alzheimer's disease using a multimodal deep learning model incorporating interaction effects.

BACKGROUND: Identifying individuals with mild cognitive impairment (MCI) at risk of progressing to A...

Learning curves for itinerant nurses to master the operation skill of Ti-robot-assisted spinal surgery equipment by CUSUM analysis: A pilot study.

This study aimed to investigate the minimum number of operations required for itinerant nurses in th...

Increasing employment opportunity for persons with spinal cord injury by digital working: an exampling case series from Thailand.

INTRODUCTION: Due to activity limitations and physical environmental barriers, low remunerative empl...

PMF-CNN: parallel multi-band fusion convolutional neural network for SSVEP-EEG decoding.

Steady-state visual evoked potential (SSVEP) is a key technique of electroencephalography (EEG)-base...

Deep Learning-Driven Exploration of Pyrroloquinoline Quinone Neuroprotective Activity in Alzheimer's Disease.

Alzheimer's disease (AD) is a pressing concern in neurodegenerative research. To address the challen...

Improving Classification Performance in Dendritic Neuron Models through Practical Initialization Strategies.

A dendritic neuron model (DNM) is a deep neural network model with a unique dendritic tree structure...

Detecting cognitive traits and occupational proficiency using EEG and statistical inference.

Machine learning (ML) is widely used in classification tasks aimed at detecting various cognitive st...

Efficient Spiking Neural Networks with Biologically Similar Lithium-Ion Memristor Neurons.

Benefiting from the brain-inspired event-driven feature and asynchronous sparse coding approach, spi...

Colorful image reconstruction from neuromorphic event cameras with biologically inspired deep color fusion neural networks.

Neuromorphic event-based cameras communicate transients in luminance instead of frames, providing vi...

Evaluating the performance of the cognitive workload model with subjective endorsement in addition to EEG.

The aptitude-oriented exercises from almost all domains impose cognitive load on their operators. Ev...

Measuring functional hand use in children with unilateral cerebral palsy using accelerometry and machine learning.

AIM: To investigate wearable sensors for measuring functional hand use in children with unilateral c...

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