Algorithm for human interaction with a model of an industrial cyber-physical system by means of neural interface

The article proposes an algorithm of a Brain Computer Interface (BCI) for implementation of interaction between a human and a model of an industrial cyberphysical system. The interface facilitates selecting a conceived tool on the basis of the classification of evoked responses of a test person’s encephalogram to visual stimuli (tool images). To conduct the study there has been designed a software system operated with a web-server, a controller, and a user BCI. The cerebral bioelectrical activity of a test person has been constantly registered with the encephalograph produced by LLC MITSAR followed by online signal processing conducted by the designed original software system. The stored evoked responses to stimuli have been classified in a variety of ways — peak-based selection, a support vector machine, and a neural net. There has been proved that accuracy of the classification of evoked potentials both with the help of a neural net and a support vector machine are approximately equal and these algorithms can be implemented in the online mode. Analysis of the experiments performed has shown that the proposed algorithm makes it possible to classify presented visual stimuli in neural interfaces in the online mode. The results show how it is possible to organize a ‘deeply integrated’ interaction between a human and an equipment through an impact of commands based on the processed signals of bioelectrical brain activity of a human on a 3D model of a production site.