Our research is focusing on the investigation and development of effective mathematical framework and learning algorithms for the analysis of brain signals, with emphasis on EEG (electroencephalographic) signal. We will try to devise statistical learning approaches to improve the accuracy for classifying brain states, improve the robustness and efficiency of learning process, and to increase the information transfer rate between brain and computer.
We believe that new user paradigms and new source of brain signals that convey complementary information need to be developed. These new paradigms and new data acquisition methods, if successful, will radically improve communication efficiency. To better harness the information provided by neuronal signals for BCI applications, we need new ways of describing brain activity to extend BCI functions to higher degrees of control. We need to make this process robust and reliable.
We also perceive that, in order to achieve the goals of BCI, it is very important to establish close collaborations with local and international organizations in multi-disciplinary areas, as the BCI research will not be fulfilled successfully without the joint contributions from neurobiology, psychology, engineering, mathematics, and computer science.
The outcome of our research will be implemented in selected applications.
