These demos show progress on EEG-based Brain-Computer Interface (BCI) systems. An EEG-based BCI enables people to communicate with the outside world by interpreting brain signals to interact with intelligent devices such as wheelchairs and robots. Motor imagery EEG (MI-EEG), which reflects a subject’s active movement intent, is a central focus in developing such systems.
A simulated robot is navigated by our system, which learns the user’s intent from EEG recordings, to take a beverage from the kitchen and bring it to the living room. Source code and dataset are available on GitHub.
Xiang Zhang, Lina Yao, Chaoran Huang, Quan Z. Sheng and Xianzhi Wang, Intent Recognition in Smart Living Through Deep Recurrent Neural Networks. The 24th International Conference On Neural Information Processing (ICONIP 2017). Guangzhou, China, November 14-18, 2017.(Accepted, CORE Rank A)
Xiang Zhang, Lina Yao, Dalin Zhang, Xianzhi Wang, Quan Z. Sheng, and Tao Gu. Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis. Mobiquitous 2017. Melbourne, Australia, November 7-10, 2017. (CORE Rank A)
An online brain typing system is developed to convert user’s thoughts to texts, which based on the high EEG (brainwave) signals classification accuracy. Motor disabled people would benefit greatly from such a system to express their thoughts and communicate with the outer world.
Xiang Zhang, Lina Yao, Quan Z. Sheng, Salil S. Kanhere, Tao Gu and Dalin Zhang, Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals. IEEE International Conference on Pervasive Computing and Communications (PerCom 2018). Athens, Greece, March 19-23, 2018. (Accepted, CORE Rank A*)