My research is focused on neural interface, including brain-machine interfaces (BMIs), neural prosthetics and dissociated neural network interface. BMIs are devices that translate signals from the cerebral cortex and use them to control a variety of outputs such as a computer cursor, prosthetic limb, exoskeleton, or electrically-stimulated muscles in a paralyzed limb. BMIs could allow patients with severe paralysis (quadriplegic or “locked-in, for example from ALS, stroke or spinal cord injury) to interact with their environment and potentially regain the use of a limb again. We are investigating the use of BMIs as a rehabilitative tool to drive changes in the brain's wiring. In addition, this technology could also provide a way for such impaired subjects to communicate by directly decoding their intended speech from the cortex. Our final goal is to optimize BMIs to the extent which they can safely and effectively be used in humans for long-term applications. Moreover, I also connected the dissociated neural network with external environment. This engineered neural network could not only allow us to investigate the fundamental principles with respect to brain, but also potentially to transplant to brain for restoring the impaired brain areas. Therefore, my research interests include the fields as below: 

1. Brain-machine Interface for rehabilitation; 2. Neural Prosthetics; 3. Dissociated Neural Network Interface; 4. Machine Learning; 5. Data Analytics.