A5046125956- EEG and Brain-Computer Interfaces
- Neural and Behavioral Psychology Studies
- Autonomous Vehicle Technology and Safety
- Virtual Reality Applications and Impacts
- Functional Brain Connectivity Studies
- Sleep and Work-Related Fatigue
- Currency Recognition and Detection
- Blind Source Separation Techniques
- Advanced Optical Imaging Technologies
- Heart Rate Variability and Autonomic Control
- Traffic and Road Safety
- ECG Monitoring and Analysis
- Visual Attention and Saliency Detection
- Neural dynamics and brain function
- Image and Video Quality Assessment
- Stochastic processes and financial applications
- Older Adults Driving Studies
- Mental Health Research Topics
Shanghai University
2023
University of Michigan
2022
Hubei University of Automotive Technology
2019-2021
Wuhan University of Technology
2019-2021
Driving style is a very important indicator and crucial measurement of driver's performance ability to drive in safe protective manner. A dangerous driving would possibly result behaviors. If the styles can be recognized by some appropriate classification methods, much attention could paid drivers with styles. The recognition module integrated into advanced assistance system (ADAS), which integrates different modules improve automation, safety comfort, then enhanced pre-warning or adjusting...
Drivers play the most important role in human-vehicle-environment system and driving behaviors are significantly influenced by cognitive state of driver his/her personality. In this paper, we aimed to explore correlation among behaviors, personality electroencephalography (EEG) using a simulated experiment. A total 36 healthy subjects participated study. The 64-channel EEG data data, including real-time position vehicle, rotation angle steering wheel speed were acquired simultaneously during...
The driver’s attentional state is a significant human factor in traffic safety. executive control process crucial sub-function of attention. To explore the relationship between driving performance and function, total 35 healthy subjects were invited to take part simulated experiment task-cuing experiment. divided into three groups according their (aberrant behaviors, including lapses errors) by clustering method. Then efficiency electroencephalogram (EEG) data acquired compared among groups....
Assessments and predictions of driving behavior are very important to improve traffic safety. We hypothesized that there were some patterns behaviors, these had correlation with cognitive states personalities. To test this hypothesis, an evaluation status, based on electroencephalography (EEG) steering in a simulated experiment, was designed performed. Unity 3D utilized design the scene. A photoelectric encoder fixed wheel corresponding data collection, transmission, storage device developed...
Abstract Driving behavior recognition is a critical part of the driver safety system. Most popular researches have utilized driver’s questionnaire data and driving to recognize behavior. But few studies used physiological different behaviors, such as EEG data. In this paper, new method was presented behaviors based on The were collected when participants took over autonomous vehicle. Through K-means, classified into two groups, classification results inputs generate k-Nearest-Neighbor model...
Cybersickness in the virtual reality (VR) environment poses a significant challenge to user experience. In this paper, we evaluate VR cybersickness quantitatively based on electroencephalography (EEG). We designed novel experimental paradigm generate rotation of user's head and collected corresponding EEG signals. A deep learning framework combing convolutional neural network (CNN) with long short-term memory (LSTM) was proposed predict cyber-sickness With method, meaningful evaluation is...
The applications of virtual reality (VR) technology are currently numerous and promising, but motion sickness (MS) problems affecting the development VR market. Questionnaires commonly used to subjectively assess sickness, they applied before after user experiences cannot user's in real time. In this work, paper proposes a convolutional neural network (CNN) structure incorporating squeeze stimulus (SE) attention mechanisms, with subjective questionnaire scores as markers...
This paper was based on the fact that calibrating option pricing models to market prices usually result in optimization issues which standard strategies (as some gradients) cannot be used.It investigated two different models: Bates's model, and Heston's stochastic volatility they both include jumps.It discusses how price options these models, as well calibrate parameters of with heuristic techniques.