A5017658038- EEG and Brain-Computer Interfaces
- Gaze Tracking and Assistive Technology
- Blind Source Separation Techniques
- Neural Networks and Applications
- Sleep and Work-Related Fatigue
- Heart Rate Variability and Autonomic Control
- Neuroscience and Neural Engineering
- Advanced Sensor and Control Systems
- Photoreceptor and optogenetics research
- Neural dynamics and brain function
- Advanced Computational Techniques and Applications
- Retinal Development and Disorders
- Radiomics and Machine Learning in Medical Imaging
- Neuroscience and Neuropharmacology Research
- Advanced materials and composites
- Neuroethics, Human Enhancement, Biomedical Innovations
- Educational Technology and Assessment
- Emotion and Mood Recognition
- Non-Invasive Vital Sign Monitoring
- Advanced Decision-Making Techniques
- User Authentication and Security Systems
- Fusion materials and technologies
- E-commerce and Technology Innovations
- Supply Chain and Inventory Management
- Advanced Battery Technologies Research
First Affiliated Hospital of Guangzhou Medical University
2020-2025
Taizhou People's Hospital
2022-2025
Nanjing Medical University
2025
Nantong University
2022-2025
Guangzhou Medical University
2020-2025
China University of Mining and Technology
2024-2025
Sichuan Agricultural University
2022-2025
Inner Mongolia Electric Power Survey & Design Institute (China)
2024
Zhejiang Sci-Tech University
2009-2024
Inner Mongolia Electric Power (China)
2024
Driver fatigue is an important contributor to road accidents, and detection has major implications for transportation safety. The aim of this research analyze the multiple entropy fusion method evaluate several channel regions effectively detect a driver's state based on electroencephalogram (EEG) records. First, we fused entropies, i.e., spectral entropy, approximate sample fuzzy as features compared with autoregressive (AR) modeling by four classifiers. Second, captured significant...
Driver fatigue has become one of the major causes traffic accidents, and is a complicated physiological process. However, there no effective method to detect driving fatigue. Electroencephalography (EEG) signals are complex, unstable, non-linear; non-linear analysis methods, such as entropy, maybe more appropriate. This study evaluates combined entropy-based processing EEG data driver In this paper, 12 subjects were selected take part in an experiment, obeying training virtual environment...
Driver fatigue has become an important factor to traffic accidents worldwide, and effective detection of driver major significance for public health. The purpose method employs entropy measures feature extraction from a single electroencephalogram (EEG) channel. Four types entropies measures, sample (SE), fuzzy (FE), approximate (AE), spectral (PE), were deployed the analysis original EEG signal compared by ten state-of-the-art classifiers. Results indicate that optimal performance channel...
Eye-tracking is an important approach to collect evidence regarding some participants' driving fatigue. In this contribution, the authors present a non-intrusive system for evaluating driver fatigue by tracking eye movement behaviours. A real-time eye-tracker was used monitor state collecting eye-movement data. These data are useful get insights into assessing during monotonous driving. Ten healthy subjects performed continuous simulated 1–2 h with monitoring on simulator in study, and these...
Purpose: Driving fatigue has become one of the important causes road accidents, there are many researches to analyze driver fatigue. EEG is becoming increasingly useful in measuring state. Manual interpretation signals impossible, so an effective method for automatic detection crucial needed. Method: In order capture principal features signals, four feature sets were computed from which fuzzy entropy (FE), sample (SE), approximate Entropy (AE), spectral (PE) and combined entropies (FE + SE...
Person authentication, based on electroencephalography (EEG) signals, is one of the directions possible in study EEG signals. In this paper, a method for selection electrodes and features discriminative manner proposed. Given that signals are unstable non-linear, non-linear analysis method, i.e., fuzzy entropy, more appropriate. unlike other methods using different signal sources patterns, such as rest state motor imagery, novel paradigm stimuli self-photos non-self-photos introduced. Ten...
This study examined whether prefrontal brain region electroencephalography (EEG) can be used to detect driver's fatigue. The participants were 13 healthy university students with driving experience. They collected EEG experiments in a virtual environment, and divided the data into normal state fatigue state. Fuzzy entropy was for feature extraction; SVM as classification tool. FP1 FP2 electrode signal selected from subject's analysis object. When single feature, accuracy of higher than FP2,...
Abstract Background Alzheimer’s disease (AD) is the most prevalent neurodegenerative with limited disease-modifying treatments. Drug repositioning strategy has now emerged as a promising approach for anti-AD drug discovery. Using 5×FAD mice and Aβ-treated neurons in culture, we tested efficacy of Y-2, compounded containing antioxidant Edaravone (Eda), pyrazolone (+)-Borneol, an anti-inflammatory diterpenoid from cinnamon, approved use amyotrophic lateral sclerosis patients. Results We...
Driver's fatigue detection, based on electroencephalography (EEG) signals, is a worthy field of research to study evidence regarding how exactly pre‐warn and avoid casualties nowadays. In this study, an EEG‐based system perfect performance good stability for evaluating driver's with only one electrode by ensemble learning method proposed. Given that EEG signals are unstable non‐linear using several common entropy measurements analyse more appropriate including spectral entropy, approximate...
Fluorine 18-labeled fibroblast activation protein inhibitor (18F-FAPI-04) positron emission tomography/computed tomography (PET/CT) has shown promise for the visualization of advanced stage lung cancer. The accuracy 18F-FAPI-04 compared with that fluorine-18 labeled-fluorodeoxyglucose (18F-FDG) in detecting early adenocarcinoma (LUAD) remains unknown. Taking surgical pathology pulmonary nodule as gold standard, diagnostic performance IA LUAD were between PET/CT and 18F-FDG PET/CT,...
The gender recognition is an important research field to study evidence regarding some personal characteristics in the information and data society. However, current traditional methods such as vision sound have been exposed their own security weaknesses. Recently, biometric based on Electroencephalography (EEG) signals has widely used safety medical fields. It necessary explore potential of using EEG present a more robust accurate result with larger training sophisticated machine learning...
In China, soil contamination by heavy metals is a widespread issue, with substantial increases in lead(Pb), cadmium(Cd), copper(Cu), and zinc(Zn) levels observed across various regions. Particularly, the concentrations of Pb Cd significantly exceed their natural background levels. P-ATPases, group proteins, utilize energy from ATP hydrolysis to support transmembrane movement metal ions. This encompasses several Heavy Metal Associated Transporter (HMA) ATPases. Studies on hyperaccumulators...
Purpose To investigate the characteristics of Mycobacterium tuberculosis (MTB)-positive population within healthcare service area Mzuzu Central Hospital in Malawi, with objective providing a scientific foundation for (TB) prevention and control strategies region.Methods This retrospective study encompassed 4,711 patients who underwent GeneXpert (GeneXpert MTB/RIF or Ultra) testing. Data on laboratory results, demographics, HIV status, residential addresses were analyzed.ResultsAmong...
Biometric based on Electroencephalogram have proved to be unique enough between subjects for applications. A new method identifying the individuality of persons by using parametric was used identification motor imagery. In this paper, autoregressive mode, phase synchronization, Energy Spectral Density and linear complexity value were as EEG features. Neural network employed individual differences. Then, rate analyzed different data length wave band. The result shows that high ratio tongue...
A research on biometry based motor imagery EEG signals was described. In this study, I select related to imagery, and a model built. Estimated parameters as feature vector were extracted, then classified by an artificial neural network. Two different classify cases, including authentication identification, investigated. Four types of three subjects compared. Experiment results show that carrying individual-specific information can be successfully exploited for purpose person identification.