Abstract: This disclosure provides an EEG decoding method based on a non-negative CP decomposition model. The method extracts time component characteristics of the EEG of the different subjects in the boundary avoidance task, optimizes a characteristic dimension by using a 2-DPCA, and takes classification by using a support vector machine, so that differences of the EEG of subjects in different states can be reflected, and the EEG classification of the single subject has a great accuracy. The time component characteristics of the EEG can be obtained by using the channel components and the frequency components based on the non-negative CP decomposition model and by means of the interaction between the EEG modes. The characteristics of the obtained EEG time components have good separability, and the dimensions of the characteristics are optimized, so that the EEG of left and right hand movements in the boundary avoidance tasks can be effectively decoded.

Abstract: The present invention provides an end-to-end solution for frontend page generation. The system includes a design module that enables a user to design UI components, an annotation submodule that enables a user to annotate the UI components with design element attributes, a database that stores database objects having database object attributes according to a data schema, a mapper module that enables a user to map design element attributes of the UI components to database object attributes without coding via a graphical user interface, a content management module that enables a user to add mapped UI components to a frontend page feed and to associate the mapped UI components with instances of the database objects, a feed generation module that retrieves instance-specific values for the database object attributes of mapped UI components in frontend page feeds, and a metadata bus that enables the modules and database to communicate.