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

Abstract

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.

EEG-Based Control for Upper and Lower Limb Exoskeletons and Prostheses: A Systematic Review

“…Therefore, to control external devices such as upper or lower limb exoskeleton in the basis of the EEG signal, the subject should generate different cortical activity patterns. The patterns include motor imagery [ 11 , 24 , 26 , 27 , 29 ] or motor execution [ 25 , 28 , 32 , 33 ], which will be recognized and translated into control commands. In the majority of current BCI, this depends on a classification algorithm [ 73 ], i.e., an algorithm developed to automatically predict the class of data as represented by a feature vector.…”

“…The first category is characterized by continual estimation of trajectories, like unscented Kalman filter (UKF) [ 25 , 28 ] and linear regression (LR) [ 75 ]. The second, category is of binary classifier such as linear discriminant analysis (LDA) [ 13 , 23 , 26 , 27 , 40 , 42 , 44 ], support vector machine (SVM) [ 12 , 38 , 42 , 46 , 54 , 55 ], logistic regression [ 33 ], random forest (RF) [ 30 ] and neural network (NN) [ 55 ].…”

“…Due to the fact that the distribution of the cortical activation throughout the MI is in the sensorimotor area, MI is sometimes named as sensorimotor rhythms (SMRs). SMR-based BCI was utilized to deliver visual feedback or to trigger assistive devices, such as lower limb exoskeleton [ 11 , 26 , 27 , 29 , 30 ], upper limb exoskeleton [ 42 , 45 , 49 , 52 , 54 ] or FES system [ 42 , 45 , 49 ]. Neuroplasticity occurs in both healthy or stroke subjects during the duration of immersive interventions which usually continued for several weeks and as a result insignificant plasticity was observed in the subject.…”

“…In addition, it is comparatively affordable and provides a high temporal resolution (about 1 ms). However, it has a weak signal and is prone to several artifacts and relatively poor spatial resolution [ 27 ]. In the EEG measurement, the detected waveforms reveal the cortical electrical activity.…”

Admittance Control of Powered Exoskeletons Based on Joint Torque Estimation

“…Conversely, an admittance function with a small gain introduces prohibitive torque to the wearer. Since we have estimated the wearer's torque, the desired angular velocity of the joint iṡ q c = P dτ h (12) where P d is the desired admittance function to be discussed shortly. Then the desired joint motor velocity isq mc = rq c , which is the velocity command to the motor.…”