Neurology

NeuroPhysNet: A FitzHugh-Nagumo-Based Physics-Informed Neural Network Framework for Electroencephalograph (EEG) Analysis and Motor Imagery Classification

Electroencephalography (EEG) is extensively employed in medical diagnostics and brain-computer interface (BCI) applications due to its non-invasive nature and high temporal resolution. However, EEG analysis faces significant challenges, including noise, nonstationarity, and inter-subject variability, which hinder its clinical utility. Traditional neural networks often lack integration with bioph...

First-in-human, real-time artificial intelligence assisted cerebral aneurysm coiling: a preliminary experience.

BACKGROUND: Neuroendovascular procedures require careful and simultaneous attention to multiple devices on multiple screens. Overlooking unintended device movements can result in complications. Advancements in artificial intelligence (AI) have enabled real-time notifications of device movements during procedures. We report our preliminary experience with real-time AI-assisted cerebral aneurysm coi...

Unraveling Parkinson's disease motor subtypes: A deep learning approach based on spatiotemporal dynamics of EEG microstates.

BACKGROUND: Despite prior studies on early-stage Parkinson's disease (PD) brain connectivity and temporal patterns, differences between tremor-dominan...

Parkinson's Disease Freezing of Gait (FoG) Symptom Detection Using Machine Learning from Wearable Sensor Data

Freezing of gait (FoG) is a special symptom found in patients with Parkinson's disease (PD). Patients who have FoG abruptly lose the capacity to wal...

Your Ride, Your Rules: Psychology and Cognition Enabled Automated Driving Systems

Despite rapid advances in autonomous driving, current autonomous vehicles (AVs) lack effective bidirectional communication with occupants, limiting ...

CLEAN-MI: A Scalable and Efficient Pipeline for Constructing High-Quality Neurodata in Motor Imagery Paradigm

The construction of large-scale, high-quality datasets is a fundamental prerequisite for developing robust and generalizable foundation models in mo...

DWI-based deep learning radiomics nomogram for predicting the impaired quality of life in patients with unruptured intracranial aneurysm developing new iatrogenic cerebral infarcts following stent placement: a multicenter cohort study.

This study developed a DWI-based radiomics nomogram to predict impaired health-related quality of life (HRQOL) in patients with unruptured intracrania...

Stroke-based Cyclic Amplifier: Image Super-Resolution at Arbitrary Ultra-Large Scales

Prior Arbitrary-Scale Image Super-Resolution (ASISR) methods often experience a significant performance decline when the upsampling factor exceeds t...

PhysioWave: A Multi-Scale Wavelet-Transformer for Physiological Signal Representation

Physiological signals are often corrupted by motion artifacts, baseline drift, and other low-SNR disturbances, which pose significant challenges for...

Vectorized Region Based Brush Strokes for Artistic Rendering

Creating a stroke-by-stroke evolution process of a visual artwork tries to bridge the emotional and educational gap between the finished static artw...

Towards Practical Alzheimer's Disease Diagnosis: A Lightweight and Interpretable Spiking Neural Model

Early diagnosis of Alzheimer's Disease (AD), especially at the mild cognitive impairment (MCI) stage, is vital yet hindered by subjective assessment...

Towards Cross-Subject EMG Pattern Recognition via Dual-Branch Adversarial Feature Disentanglement

Cross-subject electromyography (EMG) pattern recognition faces significant challenges due to inter-subject variability in muscle anatomy, electrode ...

The Predictive Brain: Neural Correlates of Word Expectancy Align with Large Language Model Prediction Probabilities

Predictive coding theory suggests that the brain continuously anticipates upcoming words to optimize language processing, but the neural mechanisms ...

Toward AI-Driven Precision Measurement of Cognition, Behavior, and Psychological Function in Aging and Alzheimer's Disease and Alzheimer's Disease-Related Dementias.

The National Institute on Aging (NIA) is at the forefront of leveraging advances in artificial intelligence (AI) to better understanding of aging and ...

CNNs improve decoding of selective attention to speech in cochlear implant users.

. Understanding speech in the presence of background noise such as other speech streams is a difficult problem for people with hearing impairment, and...

SHAP-Driven Feature Analysis Approach for Epileptic Seizure Prediction.

Predicting epileptic seizures presents a substantial difficulty in healthcare, with considerable implications for enhancing patient outcomes and quali...

Active Learning-Guided Hit Optimization for the Leucine-Rich Repeat Kinase 2 WDR Domain Based on In Silico Ligand-Binding Affinities.

The leucine-rich repeat kinase 2 (LRRK2) is the most mutated gene in familial Parkinson's disease, and its mutations lead to pathogenic hallmarks of t...

Decoding the Structure-Activity Relationship of the Dopamine D3 Receptor-Selective Ligands Using Machine and Deep Learning Approaches.

Dysfunctions of the dopamine D2 and D3 receptors (D2 and D3) are implicated in neuropsychiatric conditions such as Parkinson's disease, schizophrenia,...

Sleep Stage Classification using Multimodal Embedding Fusion from EOG and PSM

Accurate sleep stage classification is essential for diagnosing sleep disorders, particularly in aging populations. While traditional polysomnograph...

A pilot study for self-guided, active robotic training of proprioception of the upper limb in chronic stroke.

BACKGROUND: Proprioceptive impairments of the upper limb are common after stroke. These impairments are not typically addressed during assessment or r...