Brain-Computer-Interface-Systems

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 repository).

Related Papers

• 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 8-12, 2018.
• 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.

Related Papers

• 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.