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The study describes approaches of direct and supervisor control of a mobile robot based on a non-invasive brain-computer interface. An interface performs electroencephalographic signal decoding, which includes several steps: filtering, artefact detection, feature extraction, and classification. In this study, a classifier with hierarchical structure was developed and applied. Description of a committee of classifiers based on neural networks and support vector machines is given. The developed classifier demonstrated accuracy 50 ± 5% of single trial decoding of four classes of imaginary fine movements. Prospects of using non-invasive brain-computer interface for control of mobile robots was described. Key applications of the system are maintenance of immobilized patients and rehabilitation procedures both in clinic and at home.

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In the paper issues of brain-computer interface applications in assistive technologies are considered in particular for robotic devices control. Noninvasive brain-computer interfaces are built based on the classification of electroencephalographic signals, which show bioelectrical activity in different zones of the brain. Such brain-computer interfaces after training are able to decode electroencephalographic patterns corresponding to different imaginary movements and patterns corresponding to different audio-visual stimulus. The requirements which must be met by brain-computer interfaces operating in real time, so that biological feedback is effective and the user's brain can correctly associate responses with events are formulated. The process of electroencephalographic signal processing in noninvasive brain-computer interface is examined including spatial and temporal filtering, artifact removal, feature selection, and classification. Descriptions and comparison of classifiers based on support vector machines, artificial neural networks, and Riemann geometry are presented. It was shown that such classifiers can provide accuracy at the level of 60-80% for recognition of imaginary movements from two to four classes. Examples of application of the classifiers to control robotic devices were presented. The approach is intended both to help healthy users to perform daily functions better and to increase the quality of life of people with movement disabilities. Tasks to increase the efficiency of technology application are formulated.