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 brainmachine interfaces.

IntroductionUse of robotic medical exoskeletons is a promising direction in the rehabilitation of patients with locomotor disorders [1,2]. One of the problems arising in the devel opment of such systems is the problem of maintenance of vertical stability during walking. The task of maintaining the vertical stability is often allotted to the human opera tor, which is inappropriate in the case of patients with lost functions in the legs.The problem of the vertical stability of the exoskele ton is solved using the following approaches: a) all moving parts of the biomechanical system are equipped with 3D axial MEMS accelerometers and gyro scopes providing calculation of the absolute values of angles determining the spatial position of the system [3]. The use of a large number of MEMS sensors in the exoskeleton makes the final product complex and expen sive. In addition, complicated calibration procedures are required; b) one of the parts of the biomechanical system is equipped with a MEMS sensor, while inclinometers installed in the joints provide the control system with information about the mutual orientation of the moving parts. The aggregate of the data detected by the MEMS sensor and the inclinometers allows the state of the sys tem to be determined. Resistive and optical sensors are used as inclinometers [2,4]. The disadvantages of this approach are the high noise in resistive and optical incli nometers caused by walking and the rapid failure of sen sors installed in the axes of coupled moving parts. The high sensitivity of optical and resistive inclinometers to vibration, as well as their low reliability in systems sub jected to mechanical vibration and impacts, is well known to manufacturers of auto electronics [5].The goal of this work was to describe a system of exoskeleton sensors consisting of a MEMS sensor and magnetic inclinometers. Magnetic sensors were chosen because of the absence of simultaneous direct contact of the sensor with the two coupled parts of the exoskeleton. This improves the noise resistance and the reliability of the system. The problems of the hardware implementation of the system and the programmatic access to sensor data are considered in detail. The results of experimental tests of the accuracy of exoskeleton monitoring and the maximal rate of information acquisition are also presented. Materials and Methods Requirements and limitations.In developing an experimental model of a medical exoskeleton at Lobachevsky Nizhny Novgorod State University (NNSU), it became necessary to design a subsystem for exoskeleton monitoring as an integral part of the exoskeleton control system. The state of the exoskeleton can be described by the following state vector (Fig. 1):The results of development of a monitoring system for a medical exoskeleton are presented. A subsystem of sensors consisting of a MEMS sensor and a group of magnetic inclinometers is described. The problem of the interface between the sensor subsystem and the exoskeleton control system is discussed. The characteristics of the dev...

Current paper describes multimodal control system of active lower limb exoskeleton with feedback, which provides switching between manual and semi-automatic modes of exoskeleton motion control in the process of movement. Channel of proportional control of exoskeleton actuators and visual feedback allow exoskeleton pilot to overcome different kinds of obstacles on the move.

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