Hardware and software complex ensuring the functioning of the interface "brain-computer"

The purpose of the research is to develop a software and hardware complex that allows optimizing the control of a robotic device based on electroencephalogram signals based on the recognition of control commands, regardless of the principle of user interaction with a computer.

Methods. Presented is a software and hardware complex capable of functioning on the basis of both synchronous and asynchronous paradigms of the "brain-computer" interface, designed for introduction into various control loops of complex human-machine systems. The hardware includes an electroencephalography signal recording board as well as a neuroharnithra with sensor electrodes. The Cyton electroencephalography signal registration board is a key element of the hardware of the developed software and hardware complex. This is an 8-channel device compatible with the OpenBCI platform, designed for high-precision registration of brain biopotentials, as well as signals of heart and muscle activity. Software developed in Python allows you to record electroencephalography signals; preprocessing electroencephalography signals when implementing synchronous and asynchronous interfaces; classify electroencephalography data; generate control signals to the robotic device.

Results. To perform the task of classifying electroencephalography data, a model of a fully connected artificial neural network, which is a multilayer per-septron (Multi-Layer Perceptron or MLP), was developed. In synchronous mode, the P300 has a potential recognition accuracy of ~ 60%. In asynchronous mode, the accuracy of the binary classification of motor images (imaginary movements of the left and right hands) was 65%. The developed complex provides the ability to work with operators without a preliminary training stage.

Conclusion. The developed complex opens up prospects for introducing the brain-computer interface into critical technological processes where high reliability of human-eyelid machine interaction is required. Further development of the system can be aimed at improving classification accuracy, expanding the number of recognizable commands and optimizing the hardware for mobile applications.

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