The effectiveness of control using a brain–computer interface (BCI) and the success of motor imagery of the upper and lower limbs was assessed in terms of the accuracy of recognition of brain EEG signals (classification accuracy) on hand, foot, and locomotion motor imagery during 10 days of training in 10 volunteers. On training day 1, mean classification accuracy was higher for locomotion imagery than foot movement imagery, while accuracy on day 2 was better for hand imagery than locomotion imagery and accuracy on day 5 was better for foot imagery than hand imagery. On average, there was a significant increase in group mean classification accuracy by training day 3 in motor imagery of the hands and feet; as training continued, classification accuracy then decreased and again increased. Classification accuracy did not change significantly during training to locomotion imagery. Assessment of the dynamics of individual changes in classification accuracy using linear trend analysis showed that training led to increased classification accuracy for three participants (hand movements and locomotion in one, feet in two) and to decreased classification accuracy in three (hand movements and locomotion in one, locomotion in the second, and foot movements in the third). Four participants – like the group mean – showed no significant changes. These results are discussed in terms of changes in the activity of brain structures during training in relation to types of motor imagery.

Personality traits (PTs) are predictors of the success of control of brain–computer interfaces (BCIs); however, it is unknown how the PTs that are optimal for BCI control changes during training. The paper for the first time analyzes the correlations between PTs and the accuracy of the classification (AC) of brain states in imagining the movements of the hands, feet, and locomotion during 10-day training of ten volunteers in BCI control. In the first 3 days of training, the AC is higher for more stressed and anxious volunteers; in the last days, for calmer ones. In the middle of the training period, AC is higher in low-demonstrativeness persons, it is more pronounced when imagining foot movements. Correlations of low demonstrativeness, as well as of foresight and self-control with AC when imagining foot movements are revealed significantly more often than when imagining hand movements and locomotions. During almost the entire period of training, AC with locomotion imagination is higher in individualists. The results make it possible to propose individually-oriented recommendations for the use of BCI based on the imagination of movements for the rehabilitation of patients with motor disorders.

A brain-spine neurointerface based on the kinesthetic imagination of foot dorsiflexion with additional activation of foot movement by Biokin robotic device (mechanotherapy), and transcutaneous electrical spinal cord stimulation (TESCS) has been developed. Accuracy of classification of EEG-signals during the neurointerface control was on average 68% and significantly increases with the addition of mechanotherapy and TESCS by 9%. The EMG activity of the tibialis anterior (TA) – the muscle, which performs dorsiflexion of the foot, significantly increased during the instruction to imagine movement compared to that during the instruction to be at rest. The addition of mechanotherapy and TESCS during the neurointerface control has a greater effect not on the increase in TA activity when imagining the movement of the ipsilateral foot, but on the decrease in TA activity at rest. The revealed effects are apparently important for the formation of adequate coordination patterns of control signals from the CNS and of muscle activity during the implementation of movements and can be used in the clinical rehabilitation of motor activity using the cortico-spinal neurointerface.

The effectiveness of brain-computer interface (BCI) control and the success of imagination of movement of the upper and lower extremities were evaluated by the accuracy of recognition of EEG signals (classification accuracy) when imagining movements of the hands, feet and locomotion during 10-day training of 10 volunteers. Averaged data of all the volunteers revealed, that, on the first day of training, the classification accuracy is higher when imagining locomotion than foot movements, on the second day – hands than locomotion, on the fifth day – feet than hands. The average values of classification accuracy when imagining movements of the hands and feet increase by the 3rd day of training, further changes are specific depending on which movement is imagined. When learning the imagination of locomotion, the accuracy of classification does not significantly change. An assessment of the dynamics of individual changes in the accuracy of classification according to linear trends showed that in three participants, training led to an increase in the accuracy of classification (of the hand movements and locomotion – in one subject, of feet – in two subjects); in other three participants – to decrease (of the movements of the hands and locomotion – in one subject, of the locomotion – in the second subject, of feet – in the third). The four participants, as well as the sample average, had no significant changes. The results are discussed in terms of changes in the activity of brain structures during learning and depending on the type of imaginary movements.

A corticospinal neural interface was developed on the basis of kinesthetic imagery of dorsiflexion of the foot complemented by the Biokin robotic limb movement device and transcutaneous electrical stimulation of the spinal cord (TESSC). The classification accuracy (CA – the proportion of correct responses) of EEG brain signals while working with the neural interface was found to average 68

It is known that success in motor imagery in controlling brain–computer interface (BCI) systems can depend on users’ personality traits, while the ratio of activity in various right- and left-hemisphere brain structures depends on personality traits. There are no reports in BCI research on how success in controlling a BCI by subjects with different personality traits is associated with interhemisphere asymmetry. We report here an analysis of associations between personality traits and the accuracy of brain signal classification on movement imagery with the right hand (RH) and left hand (LH) as compared with the resting state in single-episode control of a BCI by naïve subjects. Motor imagery of the RH was more successful by expressive, sensitive extraverts, while motor imagery of the LH was more successful by practical reserved, skeptical, and less sociable people. People open to change were better able to control the BCI by motor imagery of both the RH and LH than traditionalists and conservatives. Analysis of the subjective difficulty of motor imagery showed that the classification accuracy of brain states on imagination of the RH, as compared with imagination of the LH, was greater in people who had greater subjective difficulty imagining RH but not LH movement. These data appear to be linked with the characteristics of information processing and movement organization and dopamine levels in the right and left hemispheres of the brain.

Brain-computer interfaces (BCIs), based on motor imagery, are increasingly used in neurorehabilitation. However, some people cannot control BCI, predictors of this are the features of brain activity and personality traits. It is not known whether the success of BCI control is related to interhemispheric asymmetry. The study was conducted on 44 BCI-naive subjects and included one BCI session, EEG-analysis, 16PF Cattell Questionnaire, estimation of latent left-handedness, and of subjective complexity of real and imagery movements. The success of brain states recognition during imagination of left hand (LH) movement compared to the rest is higher in reserved, practical, skeptical, and not very sociable individuals. Extraversion, liveliness, and dominance are significant for the imagination of right hand (RH) movements in "pure" right-handers, and sensitivity in latent left-handers. Subjective complexity of real LH and of imagery RH movements correlates with the success of brain states recognition in the imagination of movement of LH compared to RH and depends on the level of handedness. Thus, the level of handedness is the factor influencing the success of BCI control. The data are supposed to be connected with hemispheric differences in motor control, lateralization of dopamine, and may be important for rehabilitation of patients after a stroke.

Personality traits of users can affect the success in controlling brain–computer interfaces (BCIs), and the activity of right and left brain structures may differ depending on personality traits. Earlier, it was not known, how the success of BCI control with different personality traits is associated with interhemispheric asymmetry. In this work, the dependence of the success of imagination of movements, estimated by the success of recognition of EEG signals during imagination of hand movements compared to rest state, on the user’s personal characteristics was studied. It is shown that in single control of BCI by naive subjects, recognition success in imagining right-hand (RH) movements was higher in expressive sensitive extroverts, and in imagining left-hand movements (LH) it was higher in practical, reserved, skeptical, and not very sociable persons. It is suggested that this phenomenon may be based on interhemispheric differences in dopamine level and in the way of encoding movement information.

A measurement system was developed to record angular movements at the joints of the lower limbs (the stance and swing phases) allowing the moments of detachment and contact of the foot with the support surface to be determined using sensors responding to linear and angular acceleration. We present an algorithm for triggering spinal cord stimulation in specified phases of the stepping cycle, addressing the flexor and extensor motor pools of the lower limbs. A means of triggering temporospatial spinal stimulation for the “paralyzed” limb from the “intact” contralateral limb simulating stimulation conditions for patients who have had cerebrovascular accidents was developed. It is proposed that this system can be used in therapeutic, therapeutic-preventative, and medical research institutions or at home to regulate and restore motor functions in humans.

This review addresses the challenge of using brain–computer interface (BCI) systems controlled by imagining lower limb movements and their use in clinical practice. There are significantly fewer studies in this area than of BCI controlled by imaginary arm movements, partly because of methodological difficulties. This review describes various modifications to BCI, most studies being directed to restoring walking function and fewer to restoring movements at the ankle joint. Restoration of locomotor function by controlling a BCI by imaginary walking to move an avatar in a virtual space is quite often used. In a number of studies, imaginary movements are accompanied by functional electrical stimulation activating the muscles carrying out the imaginary movements or triggering movements of mechanical devices (orthoses, exoskeletons). Finally, fundamentally new integrative approaches are described, such as BCI controlled by imaginary arm movements generating signals triggering movements of the avatar’s leg or an orthosis, with tactile stimulation of the forearm with the stepping rhythm. The literature contains no studies on the challenge of restoring lower limb functions in Russia.