A5082674612- EEG and Brain-Computer Interfaces
- Gaze Tracking and Assistive Technology
- Advanced Memory and Neural Computing
- Stroke Rehabilitation and Recovery
- Neuroscience and Neural Engineering
- Neural dynamics and brain function
- Transcranial Magnetic Stimulation Studies
- Digital Transformation in Industry
- Additive Manufacturing and 3D Printing Technologies
- Advanced Algorithms and Applications
- Visual perception and processing mechanisms
- Tactile and Sensory Interactions
- Advanced Sensor and Control Systems
- Muscle activation and electromyography studies
- Manufacturing Process and Optimization
Beijing Institute of Technology
2022-2025
Brain-computer interfaces (BCIs) have been used in two-dimensional (2D) navigation robotic devices, such as brain-controlled wheelchairs and vehicles. However, contemporary BCI systems are driven by binary selective control. On the one hand, only directional information can be transferred from humans to machines, "turn left" or right", which means that quantified value, radius of gyration, cannot controlled. In this study, we proposed a spatial gradient controller corresponding environment...
With the rapid development of internet things (IoT) systems, application potential remote-oriented unmanned aerial vehicle (UAV) in IoT systems is becoming increasingly prominent. Brain-computer interface (BCI)-based UAV can not only leverage natural advantages human brain cognition and response, but also contribute to safer more efficient operations certain special environments. However, BCI still face challenges spatial perception control capabilities. In this study, a...
The brain–computer interface (BCI) technology has received lots of attention in the field scientific research because it can help disabled people improve their quality life. Steady‐state visual evoked potential (SSVEP) is most researched BCI experimental paradigm, which offers advantages high signal‐to‐noise ratio and short training‐time requirement by users. In a complete system, two critical components are paradigm decoding algorithm. However, systematic combination SSVEP algorithms...
Advancements in brain-machine interfaces (BMIs) have led to the development of novel rehabilitation training methods for people with impaired hand function. However, contemporary exoskeleton systems predominantly adopt passive control methods, leading low system performance. In this work, an active brain-controlled is proposed that uses a augmented reality-fused stimulus (AR-FS) paradigm as human-machine interface, which enables users actively their fingers move. Considering AR-FS generates...
Abstract The past two decades have witnessed dramatic advancement in computer‐aided design (CAD). However, development of human–computer interfaces (HCI) for CAD not kept up with these advances. Windows, Icons, Menus, Pointer (WIMP) is still the mainly used interface applications which limits naturalness and intuitiveness modeling process. As a novel interface, Brain–computer (BCIs) great potential application modeling. Utilizing BCIs, user can create models just by thinking about it...
Abstract Advances in brain-machine interfaces (BMIs) have greatly enhanced the control of assistive devices like hand exoskeletons, providing new rehabilitation possibilities for individuals with impaired function. However, presence cognitive distraction during device operation may negatively affect performance. This study explores impact interference on a brain-controlled exoskeleton system. Participants were required to perform movement tasks using while engaging task. The results showed...