This series of demos demonstrate our recent progress in regards to Brain Computer Interface (BCI) system. An
electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the
outside world by interpreting the EEG signals of their brains to interact with intelligent devices such as
wheelchairs and robots. More specifically, motor imagery EEG (MI-EEG), which reflects a subject’s active
movement intent, has been attracting increasing attention in developing an EEG-based BCI system.
Mind control smart living
A simulated robot is navigated by our system, which learns user’s intent from EEG recordings, to take a can
of beverage from a table in the kitchen and put it in a table in living room. Reusable source code and
dataset are provided in the github (EEG-based-Control
Weitong Chen, Sen Wang, Guodong Long, Lina Yao, Quan Zheng Sheng, and Xue Li, Dynamic Illness
Severity Prediction via Multi-task RNNs for Intensive Care Unit. IEEE International Conference
on Data Mining (ICDM 2018). Singapore, November 17-20, 2018.
Dalin Zhang, Lina Yao, Kaixuan Chen, Sen Wang, Ready for Use: Subject-Independent Movement
Intention Recognition via A Convolutional Attention Model. The 27th ACM Conference on
Information and Knowledge Management (CIKM 2018). Lingotto, Turin, Italy, October 22 - 26, 2018
Xiang Zhang,Lina Yao, Salil.S. Kanhere, Yunhao Liu, Tao Gu and Kaixuan Chen, MindID: Person
Identification from Brain Waves through Attention-based Recurrent Neural Network. 2018 ACM
International Joint Conference on Pervasive and Ubiquitous Computing (Ubicomp 2018). Singapore, October
Dalin Zhang,Lina Yao, Sen Wang, Kaixuan Chen, Zheng Yang and Boualem Benatallah, Fuzzy Integral
Optimization with Deep Q-Network for EEG-based Intention Recognition. The 22nd Pacific-Asia
Conference on Knowledge Discovery and Data Mining (PAKDD 2018). Melbourne, Australia, June 3 - 6, 2018.
Dalin Zhang,Lina Yao, Xiang Zhang, Sen Wang, Weitong Chen and Robert Boots,EEG-based Intention
Recognition from Spatio-Temporal Representations via Cascade and Parallel Convolutional Recurrent Neural
Networks. The 32nd AAAI Conference on Artifical Intelligence (AAAI-18), New Orleans, Lousiana,
USA, 2 - 7 February, 2018
Xiang Zhang,Lina Yao, Dalin Zhang, Xianzhi Wang, Quan Z. Sheng and Tao Gu,Multi-Person Brain
Activity Recognition via Comprehensive EEG Signal Analysis. The 14th International Conference
on Mobile and Ubiquitous Systems: Computing, Networking and Services (Mobiquitous 2017), Melbourne,
Australia Nov 7 - 10, 2017.
Xiang Zhang,Lina Yao, Chaoran Huang, QuanZheng 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.
Brain Typing System
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. The
EEG dataset can be accessed from this link.
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