Abstract
Many scientists are interested in the analysis of human’s brainwave using electroencephalography (EEG) and conducting several applications such as game-controlling and physical therapy based on the EEG analysis. A brain-computer interface (BCI) could capture subjects’ EEG signals and to some extent infer the subject’s intention by analyzing the signals in a back-end system. In my thesis, we propose to use a novel kind of EEG analysis that is called error-related potential (ErrP) to enhance the performance and applicability of BCIs. ErrP is based on the fact that human’s cognitive state can be aware of error, and the unique kind of brainwave will be produced to reflect this cognitive state called ErrP which belongs to a kind of event-related potential (ERP). For conquering the low information rate of brain-computer interface, we use the shared-control strategy to enhance the robustness of our proposed model. Therefore, we combine the ErrP from human subject and shared-control strategy to propose a new kind model: Drawing model, and use the moving method : Convince and Reduce search (RAC search) we proposed to make the object move on the drawing model according to the rule of RAC search as system receive the response EEG signal of human subject. Because the accuracy of each subject’s personal trained classifier is not 100%, therefore, the object might deviate from the normal track while the classifier make a misclassification. So we add a probability mechanism for backtracking the out-of-track object to the normal track as the system has enough confidence. Our objective is to make the object complete the drawing mission quickly by using our drawing model. In the last, we will conduct a series of analysis for our experimental results, and the results show that our proposed drawing model enhance about 16% performance by comparing to the try and error model which is without the influence of subjects’ brainwave.