Zhizeng Luo
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Motor Imagery EEG Classification Based on Multi-Domain Feature Rotation and Stacking Ensemble Open
Background: Decoding motor intentions from electroencephalogram (EEG) signals is a critical component of motor imagery-based brain–computer interface (MI–BCIs). In traditional EEG signal classification, effectively utilizing the valuable i…
Motor Imagery EEG Classification Based on Multi-Domain Feature rotation and Stacking Ensemble Open
Decoding motor intentions from electroencephalogram (EEG) signals is a critical component of motor imagery-based brain-computer interfaces (MI-BCIs). In traditional EEG signal classification, effectively utilizing the valuable information …
[Analysis of the effect of neuromuscular electrical stimulation on corticomuscular coupling during standing balance]. Open
Neuromuscular electrical stimulation (NMES) has been proven to promote human balance, but research on its impact on motor ability mainly focuses on external physical analysis, with little analysis on the intrinsic neural regulatory mechani…
Charged Gold Nanoparticles for Target Identification–Alignment and Automatic Segmentation of CT Image-Guided Adaptive Radiotherapy in Small Hepatocellular Carcinoma Open
Because of the challenges posed by anatomical uncertainties and the low resolution of plain computed tomography (CT) scans, implementing adaptive radiotherapy (ART) for small hepatocellular carcinoma (sHCC) using artificial intelligence (A…
Application of a Multi-Source Multi-Task Weight Adaptation Framework for Cross-Domain EEG Emotion Recognition(MS-MWA) Open
In the practical implementation of the Electroencephalography (EEG) emotion recognition system, the development of a universal model applicable to different subjects and sessions is crucial. Previous studies have primarily focused on emplo…
Research on Brain Networks of Human Balance Based on Phase Estimation Synchronization Open
Phase synchronization serves as an effective method for analyzing the synchronization of electroencephalogram (EEG) signals among brain regions and the dynamic changes of the brain. The purpose of this paper is to study the construction of…
Skin marker combined with surface‐guided auto‐positioning for breast DIBH radiotherapy daily initial patient setup: An optimal schedule for both accuracy and efficiency Open
Background and purpose By employing three surface‐guided radiotherapy (SGRT)‐assisted positioning methods, we conducted a prospective study of patients undergoing SGRT‐based deep inspiration breath‐hold (DIBH) radiotherapy using a Sentine/…
Influence of Anodal tDCS on the Brain Functional Networks and Muscle Synergy of Hand Movements Open
Background: Transcranial direct current stimulation (tDCS) is a non-invasive technique that has demonstrated potential in modulating cortical neuron excitability. The objective of this paper is to investigate the effects of tDCS on charact…
Analysis of Cortico-Muscular Coupling and Functional Brain Network under Different Standing Balance Paradigms Open
Maintaining standing balance is essential for people to engage in productive activities in daily life. However, the process of interaction between the cortex and the muscles during balance regulation is understudied. Four balance paradigms…
Advances in nanotechnology-based targeted-contrast agents for computed tomography and magnetic resonance Open
X-ray computed tomography (CT) and magnetic resonance (MR) imaging are essential tools in modern medical diagnosis and treatment. However, traditional contrast agents are inadequate in the diagnosis of various health conditions. Consequent…
Chinese sign language recognition based on surface electromyography and motion information Open
Sign language (SL) has strong structural features. Various gestures and the complex trajectories of hand movements bring challenges to sign language recognition (SLR). Based on the inherent correlation between gesture and trajectory of SL …
SGRT‐based DIBH radiotherapy practice for right‐sided breast cancer combined with RNI: A retrospective study on dosimetry and setup accuracy Open
Background We retrospectively studied the dosimetry and setup accuracy of deep inspiration breath‐hold (DIBH) radiotherapy in right‐sided breast cancer patients with regional nodal irradiation (RNI) who had completed treatment based on sur…
Research on the Effect of MT+FES Training on Sensorimotor Cortex Open
Purpose. Aiming at the motor recovery of patients with unilateral upper limb motor dysfunction after stroke, we propose a mirror therapy (MT) training method, which uses surface electromyography (sEMG) to identify movements on one side and…
Research on AR-AKF Model Denoising of the EMG Signal Open
Electromyography (EMG) signals can be used for clinical diagnosis and biomedical applications. It is very important to reduce noise and to acquire accurate signals for the usage of the EMG signals in biomedical engineering. Since EMG signa…
Research on Recognition of Motor Imagination Based on Connectivity Features of Brain Functional Network Open
Feature extraction is essential for classifying different motor imagery (MI) tasks in a brain-computer interface. To improve classification accuracy, we propose a novel feature extraction method in which the connectivity increment rate (CI…
Optimal channel-based sparse time-frequency blocks common spatial pattern feature extraction method for motor imagery classification Open
Common spatial pattern (CSP) as a spatial filtering method has been most widely applied to electroencephalogram (EEG) feature extraction to classify motor imagery (MI) in brain-computer interface (BCI) applications. The effectiveness of CS…
Gesture Recognition Based on Multiscale Singular Value Entropy and Deep Belief Network Open
As an important research direction of human–computer interaction technology, gesture recognition is the key to realizing sign language translation. To improve the accuracy of gesture recognition, a new gesture recognition method based on f…
Movement Trajectory Recognition of Sign Language Based on Optimized Dynamic Time Warping Open
Movement trajectory recognition is the key link of sign language (SL) translation research, which directly affects the accuracy of SL translation results. A new method is proposed for the accurate recognition of movement trajectory. First,…
Driving Drowsiness Detection with EEG Using a Modified Hierarchical Extreme Learning Machine Algorithm with Particle Swarm Optimization: A Pilot Study Open
Driving fatigue accounts for a large number of traffic accidents in modern life nowadays. It is therefore of great importance to reduce this risky factor by detecting the driver’s drowsiness condition. This study aimed to detect drivers’ d…
View article: Double-Criteria Active Learning for Multiclass Brain-Computer Interfaces
Double-Criteria Active Learning for Multiclass Brain-Computer Interfaces Open
Recent technological advances have enabled researchers to collect large amounts of electroencephalography (EEG) signals in labeled and unlabeled datasets. It is expensive and time consuming to collect labeled EEG data for use in brain-comp…
sEMG-MMG State-Space Model for the Continuous Estimation of Multijoint Angle Open
Continuous joint angle estimation plays an important role in motion intention recognition and rehabilitation training. In this study, a surface electromyography- (sEMG-) mechanomyography (MMG) state-space model is proposed to estimate cont…
EEG Feature Extraction Based on a Bilevel Network: Minimum Spanning Tree and Regional Network Open
Feature extraction is essential for classifying different motor imagery (MI) tasks in a brain–computer interface (BCI). Although the methods of brain network analysis have been widely studied in the BCI field, these methods are limited by …
Daily Activity Monitoring and Fall Detection Based on Surface Electromyography and Plantar Pressure Open
Falls among the elderly comprise a major health problem. Daily activity monitoring and fall detection using wearable sensors provide an important healthcare system for elderly or frail individuals. We investigated the classification accura…
Decoding EEG in Motor Imagery Tasks with Graph Semi-Supervised Broad Learning Open
In recent years, the accurate and real-time classification of electroencephalogram (EEG) signals has drawn increasing attention in the application of brain-computer interface technology (BCI). Supervised methods used to classify EEG signal…
SEMG-based multifeatures and predictive model for knee-joint-angle estimation Open
Surface electromyography (sEMG) signals are commonly used in activity monitoring and rehabilitation training as they reflect effectively the motor intentions of users. This study proposed a new sEMG-based multifeature extraction and predic…
Driving Fatigue Detection from EEG Using a Modified PCANet Method Open
The rapid development of the automotive industry has brought great convenience to our life, which also leads to a dramatic increase in the amount of traffic accidents. A large proportion of traffic accidents were caused by driving fatigue.…
Time-Frequency-Domain Copula-Based Granger Causality and Application to Corticomuscular Coupling in Stroke Open
The corticomuscular coupling (CMC) characterization between the motor cortex and muscles during motion control is a valid biomarker of motor system function after stroke, which can improve clinical decision-making. However, traditional CMC…
Feature-Level Fusion of Surface Electromyography for Activity Monitoring Open
Surface electromyography (sEMG) signals are commonly used in activity monitoring and rehabilitation applications as they reflect effectively the motor intentions of users. However, real-time sEMG signals are non-stationary and vary to a la…