Neurology

Application of an EMG-Rehabilitation Robot in Patients with Post-Coronavirus Fatigue Syndrome (COVID-19)-A Feasibility Study.

This pilot study aimed to assess the safety and feasibility of an EMG-driven rehabilitation robot in patients with Post-Viral Fatigue (PVF) syndrome after COVID-19. The participants were randomly assigned to two groups (IG-intervention group and CG-control group) in an inpatient neurological rehabilitation unit. Both groups were assessed on admission and after six weeks of rehabilitation. Rehabili...

CNN-XGBoost fusion-based affective state recognition using EEG spectrogram image analysis.

Recognizing emotional state of human using brain signal is an active research domain with several open challenges. In this research, we propose a signal spectrogram image based CNN-XGBoost fusion method for recognising three dimensions of emotion, namely arousal (calm or excitement), valence (positive or negative feeling) and dominance (without control or empowered). We used a benchmark dataset ca...

On the Deep Learning Models for EEG-Based Brain-Computer Interface Using Motor Imagery.

Motor imagery (MI) based brain-computer interface (BCI) is an important BCI paradigm which requires powerful classifiers. Recent development of deep l...

Influence of internal jugular vein catheterisation on optic nerve sheath diameter in patients undergoing robot-assisted laparoscopic surgery: a prospective cohort study.

Deep-Learning-Based Automatic Selection of Fewest Channels for Brain-Machine Interfaces.

Due to the development of convenient brain-machine interfaces (BMIs), the automatic selection of a minimum channel (electrode) set has attracted incre...

Automated Magnetic Resonance Image Segmentation of Spinal Structures at the L4-5 Level with Deep Learning: 3D Reconstruction of Lumbar Intervertebral Foramen.

OBJECTIVE: 3D reconstruction of lumbar intervertebral foramen (LIVF) has been beneficial in evaluating surgical trajectory. Still, the current methods...

Robotics is useful for less-experienced surgeons in spinal deformity surgery.

PURPOSE: To verify whether robotics was useful for surgeons who had less experience with spinal deformity surgery.

Simultaneously exploring multi-scale and asymmetric EEG features for emotion recognition.

In recent years, emotion recognition based on electroencephalography (EEG) has received growing interests in the brain-computer interaction (BCI) fiel...

Deep learning-based automated segmentation of resection cavities on postsurgical epilepsy MRI.

Accurate segmentation of surgical resection sites is critical for clinical assessments and neuroimaging research applications, including resection ext...

Interpretable Artificial Intelligence through Locality Guided Neural Networks.

In current deep learning architectures, each of the deeper layers in networks tends to contain hundreds of unorganized neurons which makes it hard for...

Classification model with weighted regularization to improve the reproducibility of neuroimaging signature selection.

Machine learning (ML) has been extensively applied in brain imaging studies to aid the diagnosis of psychiatric disorders and the selection of potenti...

Accuracy of artificial intelligence for the detection of intracranial hemorrhage and chronic cerebral microbleeds: a systematic review and pooled analysis.

BACKGROUND: Artificial intelligence (AI)-driven software has been developed and become commercially available within the past few years for the detect...

Deep learning methods to predict amyotrophic lateral sclerosis disease progression.

Amyotrophic lateral sclerosis (ALS) is a highly complex and heterogeneous neurodegenerative disease that affects motor neurons. Since life expectancy ...

Evaluation of auto-segmentation for EBRT planning structures using deep learning-based workflow on cervical cancer.

Deep learning (DL) based approach aims to construct a full workflow solution for cervical cancer with external beam radiation therapy (EBRT) and brach...

Deep learning prediction of chemical-induced dose-dependent and context-specific multiplex phenotype responses and its application to personalized alzheimer's disease drug repurposing.

Predictive modeling of drug-induced gene expressions is a powerful tool for phenotype-based compound screening and drug repurposing. State-of-the-art ...

eyeSay: Brain Visual Dynamics Decoding With Deep Learning & Edge Computing.

Brain visual dynamics encode rich functional and biological patterns of the neural system, and if decoded, are of great promise for many applications ...

Deep Learning Estimation of Median Nerve Volume Using Ultrasound Imaging in a Human Cadaver Model.

Median nerve swelling is one of the features of carpal tunnel syndrome (CTS), and ultrasound measurement of maximum median nerve cross-sectional area ...

Effective Evaluation of Medical Images Using Artificial Intelligence Techniques.

This work is implemented for the management of patients with epilepsy, and methods based on electroencephalography (EEG) analysis have been proposed f...

Predicting intraoperative hypotension using deep learning with waveforms of arterial blood pressure, electroencephalogram, and electrocardiogram: Retrospective study.

To develop deep learning models for predicting Interoperative hypotension (IOH) using waveforms from arterial blood pressure (ABP), electrocardiogram ...

A Transformer-Based Approach Combining Deep Learning Network and Spatial-Temporal Information for Raw EEG Classification.

The attention mechanism of the Transformer has the advantage of extracting feature correlation in the long-sequence data and visualizing the model. As...