Neurology

Attention-based convolutional neural network with multi-modal temporal information fusion for motor imagery EEG decoding.

Convolutional neural network (CNN) has been widely applied in motor imagery (MI)-based brain computer interface (BCI) to decode electroencephalography (EEG) signals. However, due to the limited perceptual field of convolutional kernel, CNN only extracts features from local region without considering long-term dependencies for EEG decoding. Apart from long-term dependencies, multi-modal temporal in...

Combined blood Neurofilament light chain and third ventricle width to differentiate Progressive Supranuclear Palsy from Parkinson's Disease: A machine learning study.

INTRODUCTION: Differentiating Progressive Supranuclear Palsy (PSP) from Parkinson's Disease (PD) may be clinically challenging. In this study, we explored the performance of machine learning models based on MR imaging and blood molecular biomarkers in distinguishing between these two neurodegenerative diseases.

Autism spectrum disorder diagnosis with EEG signals using time series maps of brain functional connectivity and a combined CNN-LSTM model.

BACKGROUND AND OBJECTIVE: People with autism spectrum disorder (ASD) often have cognitive impairments. Effective connectivity between different areas ...

Study on Gesture Recognition Method with Two-Stream Residual Network Fusing sEMG Signals and Acceleration Signals.

Currently, surface EMG signals have a wide range of applications in human-computer interaction systems. However, selecting features for gesture recogn...

A Mouth and Tongue Interactive Device to Control Wearable Robotic Limbs in Tasks where Human Limbs Are Occupied.

The Wearable Robotic Limb (WRL) is a type of robotic arm worn on the human body, aiming to enhance the wearer's operational capabilities. However, pro...

Transfer learning and self-distillation for automated detection of schizophrenia using single-channel EEG and scalogram images.

Schizophrenia (SZ) has been acknowledged as a highly intricate mental disorder for a long time. In fact, individuals with SZ experience a blurred line...

Machine Learning-Based Perivascular Space Volumetry in Alzheimer Disease.

OBJECTIVES: Impaired perivascular clearance has been suggested as a contributing factor to the pathogenesis of Alzheimer disease (AD). However, it rem...

Application of interpretable machine learning algorithms to predict acute kidney injury in patients with cerebral infarction in ICU.

BACKGROUND: Acute kidney injury (AKI) is not only a complication but also a serious threat to patients with cerebral infarction (CI). This study aimed...

Application of Artificial Intelligence in Tissue Engineering.

Tissue engineering, a crucial approach in medical research and clinical applications, aims to regenerate damaged organs. By combining stem cells, bioc...

How robot-assisted gait training affects gait ability, balance and kinematic parameters after stroke: a systematic review and meta-analysis.

INTRODUCTION: Gait ability is often cited by stroke survivors. Robot-assisted gait training (RAGT) can help stroke patients with lower limb motor impa...

Physics-Informed Transfer Learning to Enhance Sleep Staging.

OBJECTIVE: At-home sleep staging using wearable medical sensors poses a viable alternative to in-hospital polysomnography due to its lower cost and lo...

External evaluation of a deep learning-based approach for automated brain volumetry in patients with huntington's disease.

A crucial step in the clinical adaptation of an AI-based tool is an external, independent validation. The aim of this study was to investigate brain a...

Streamlining neuroradiology workflow with AI for improved cerebrovascular structure monitoring.

Radiological imaging to examine intracranial blood vessels is critical for preoperative planning and postoperative follow-up. Automated segmentation o...

Classification of mental workload using brain connectivity and machine learning on electroencephalogram data.

Mental workload refers to the cognitive effort required to perform tasks, and it is an important factor in various fields, including system design, cl...

Artificial Intelligence Assistance for the Measurement of Full Alignment Parameters in Whole-Spine Lateral Radiographs.

BACKGROUND: Measuring spinal alignment with radiological parameters is essential in patients with spinal conditions likely to be treated surgically. T...

Migraine aura discrimination using machine learning: an fMRI study during ictal and interictal periods.

Functional magnetic resonance imaging (fMRI) studies on migraine with aura are challenging due to the rarity of patients with triggered cases. This st...

A single-joint multi-task motor imagery EEG signal recognition method based on Empirical Wavelet and Multi-Kernel Extreme Learning Machine.

BACKGROUND: In the pursuit of finer Brain-Computer Interface commands, research focus has shifted towards classifying EEG signals for multiple tasks. ...

Advancing post-traumatic seizure classification and biomarker identification: Information decomposition based multimodal fusion and explainable machine learning with missing neuroimaging data.

A late post-traumatic seizure (LPTS), a consequence of traumatic brain injury (TBI), can potentially evolve into a lifelong condition known as post-tr...

ATST-Net: A method to identify early symptoms in the upper and lower extremities of PD.

Bradykinesia, a core symptom of motor disorders in Parkinson's disease (PD), is a major criterion for screening early PD patients in clinical practice...

Socially Assistive Robot for Stroke Rehabilitation: A Long-Term in-the-Wild Pilot Randomized Controlled Trial.

Socially assistive robots (SARs) have been suggested as a platform for post-stroke training. It is not yet known whether long-term interaction with a ...