The Mathematical Model And Algorthem For Brain-Computer Interface Based On Motor Imagery

Brain computer interface (Brain-computer interface, BCI) is an information channel between brain and external devices, transfer of demand information. It can bring people demand directly into the external command, which replaces the limb or language and realizes the communication with the outside world.BCI this technology will be more. Deaf patients to provide convenient communication tools. This thesis focuses on the research of BCI model and algorithm of motor imagery, the main aim is to find and define the most able to distinguish different types of motor imagery EEG signal quantity characteristic, classification algorithm and constructs the appropriate, get the recognition classifier is higherThe full text is divided into seven chapters:The first chapter mainly introduces the research background and the development prospects of the BCI, and introduces the main work of this paper.The second chapter introduces the basic knowledge related to this study, including EEG signals in the frequency domain analysis method.The third chapter introduces the classification and characteristics of electrode screening first-order wave level based on the task, and then correlation analysis, and then according to the first average "method and second average accuracy of the definition of the corresponding method.The fourth chapter mainly discusses the concept of two order wave level, as well as the analysis of the experimental data using the two order wave.The fifth chapter mainly introduces the concept and algorithm of distance.The sixth chapter mainly introduces the concept and algorithm of distance.The seventh chapter is divided into three parts, including introduction to the CSP classification algorithm, and analyze the experimental data and results analysis.