Exoskeleton Control System Based on Motor-Imaginary Brain–Computer Interface

The aim of the investigation was to develop the neuro-integrated control system for a lower-limb robotic exoskeleton (RE) using brain–computer interface (BCI) technology based on recognition of EEG patterns evoked by motor imagery of limb movement.

Materials and Methods. The proposed neuro-integrated RE control system based on BCI technology consists of three main modules: EEG signal recording module, EEG signal classifier and the software for transmission of commands to RE. EEG patterns evoked by motor imagery are recognized by the classifier based on linear discriminant analysis that uses the features identified by spatial filtering applying CSP method for all types of commands pairwise. The proposed algorithms for classification of motor imagery patterns and user training techniques make it possible to reliably distinguish several (up to 4) different commands. After training and testing the classifier, the operator may proceed to control the external device, i.e. the lower-limb RE. RE control software has been developed for easy system customization. The software has a simple graphical user interface and allows the user to change the mapping of RE patterns and commands in the operation process.

Results. As a result of testing in 14 healthy volunteers, the average accuracy of lower limb exoskeleton control based on the developed motor imagery BCI for three commands was found to average 70% in three sessions.

Conclusion. The developed RE control system based on BCI technology offers fairly high accuracy for three commands. The operators successfully learn to practice motor imagery and operate the BCI contour, even if they have no previous experience of work with brain–machine interfaces.

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Gordleeva S.Yu., Lukoyanov M.V., Mineev S.A., Khoruzhko M.A., Mironov V.I., Kaplan A.Ya., Kazantsev V.B. Exoskeleton Control System Based on Motor-Imaginary Brain–Computer Interface. Sovremennye tehnologii v medicine 2017; 9(3): 31, https://doi.org/10.17691/stm2017.9.3.04