Neurology

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

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PD-ARnet: a deep learning approach for Parkinson's disease diagnosis from resting-state fMRI.

. The clinical diagnosis of Parkinson's disease (PD) relying on medical history, clinical symptoms, ...

Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces.

Training an accurate classifier for EEG-based brain-computer interface (BCI) requires EEG data from ...

Accurate Machine Learning-based Monitoring of Anesthesia Depth with EEG Recording.

General anesthesia, pivotal for surgical procedures, requires precise depth monitoring to mitigate r...

A Machine Learning Approach to Concussion Risk Estimation Among Players Exhibiting Visible Signs in Professional Hockey.

BACKGROUND: The identification of concussion risk factors, such as visible signs and mechanisms of i...

Using deep learning and pretreatment EEG to predict response to sertraline, bupropion, and placebo.

OBJECTIVE: Predicting an individual's response to antidepressant medication remains one of the most ...

Sensory Stimulation and Robot-Assisted Arm Training After Stroke: A Randomized Controlled Trial.

BACKGROUND AND PURPOSE: Functional recovery after stroke is often limited, despite various treatment...

Implications of the novel EU AI Act for neurotechnologies.

The EU AI Act, the first comprehensive regulation of AI, came into effect in August. Here, we provid...

Predicting stroke volume variation using central venous pressure waveform: a deep learning approach.

. This study evaluated the predictive performance of a deep learning approach to predict stroke volu...

Decoding Multi-Class Motor Imagery From Unilateral Limbs Using EEG Signals.

The EEG is a widely utilized neural signal source, particularly in motor imagery-based brain-compute...

Mitigating the Concurrent Interference of Electrode Shift and Loosening in Myoelectric Pattern Recognition Using Siamese Autoencoder Network.

The objective of this work is to develop a novel myoelectric pattern recognition (MPR) method to mit...

AI-driven innovations in Alzheimer's disease: Integrating early diagnosis, personalized treatment, and prognostic modelling.

Alzheimer's disease (AD) presents a significant challenge in neurodegenerative research and clinical...

Criticality of Nursing Care for Patients With Alzheimer's Disease in the ICU: Insights From MIMIC III Dataset.

Alzheimer's disease (AD) patients admitted to intensive care units (ICUs) exhibit varying survival o...

Neuromorphic intermediate representation: A unified instruction set for interoperable brain-inspired computing.

Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are gett...

Machine learning approaches to identify the link between heavy metal exposure and ischemic stroke using the US NHANES data from 2003 to 2018.

PURPOSE: There is limited understanding of the link between exposure to heavy metals and ischemic st...

A Learnable and Explainable Wavelet Neural Network for EEG Artifacts Detection and Classification.

Electroencephalography (EEG) artifacts are very common in clinical diagnosis and can heavily impact ...

A Strong and Simple Deep Learning Baseline for BCI Motor Imagery Decoding.

We propose EEG-SimpleConv, a straightforward 1D convolutional neural network for Motor Imagery decod...

A Novel Bilateral Underactuated Upper Limb Exoskeleton for Post-Stroke Bimanual ADL Training.

This paper introduces a lightweight bilateral underactuated upper limb exoskeleton (UULE) designed t...

A Spatio-Temporal Capsule Neural Network with Self-Correlation Routing for EEG Decoding of Semantic Concepts of Imagination and Perception Tasks.

Decoding semantic concepts for imagination and perception tasks (SCIP) is important for rehabilitati...

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