Lower limb muscle activity during neurointerface control: neurointerface based on motor imagery of walking

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Abstract

The question of the activity of muscles that provide the realization of imaginary movement is essential in the rehabilitation of motor disorders using neurointerfaces. The literature data on this issue are contradictory. The paper analyzes the EMG activity of the shin and thigh muscles of 40 healthy volunteers when working with a neurointerface based on kinesthetic motor imagery of walking in place and supplemented with the «Biokin» robotic limb movement device (mechanotherapy), activated in case of successful motor imagery. It is shown that working with a neurointerface, on average for subjects, leads to an increase in muscle activity when motor imagery of walking compared to rest, and activation of the mechanical training device (AM) further increases muscle activity, with its effect being more pronounced in the muscles of the leg from which motor imagery of walking begins. The nature of muscle reactions to the task of motor imagery of walking is individual. AM when working with a neurointerface, the number of subjects with pronounced EMG activity increases, as does the number of significant correlations between the activity of the muscles of the lower limbs. Thus, the use of neurointerfaces based on motor imagery of walking and the addition of AM as feedback allows activating the muscles of the lower extremities, which is important in clinical practice in the rehabilitation of movements.

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About the authors

E. V. Bobrova

Pavlov Institute of Physiology, Russian Academy of Sciences

Author for correspondence.
Email: eabobrovy@gmail.com
Russian Federation, St. Petersburg

V. V. Reshetnikova

Pavlov Institute of Physiology, Russian Academy of Sciences

Email: eabobrovy@gmail.com
Russian Federation, St. Petersburg

A. A. Grishin

Pavlov Institute of Physiology, Russian Academy of Sciences

Email: eabobrovy@gmail.com
Russian Federation, St. Petersburg

E. A. Vershinina

Pavlov Institute of Physiology, Russian Academy of Sciences

Email: eabobrovy@gmail.com
Russian Federation, St. Petersburg

I. N. Bogacheva

Pavlov Institute of Physiology, Russian Academy of Sciences

Email: eabobrovy@gmail.com
Russian Federation, St. Petersburg

N. A. Chsherbakova

Pavlov Institute of Physiology, Russian Academy of Sciences

Email: eabobrovy@gmail.com
Russian Federation, St. Petersburg

M. R. Isaev

Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences; Institute of Translational Medicine of Pirogov of Russian National Research Medical University

Email: eabobrovy@gmail.com
Russian Federation, Moscow; Moscow

P. D. Bobrov

Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences; Institute of Translational Medicine of Pirogov of Russian National Research Medical University

Email: eabobrovy@gmail.com
Russian Federation, Moscow; Moscow

Y. P. Gerasimenko

Pavlov Institute of Physiology, Russian Academy of Sciences

Email: eabobrovy@gmail.com
Russian Federation, St. Petersburg

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2. Fig. 1. The scheme of operation of the complex based on a neurointerface with visual feedback, supplemented by the «Biokin» mechanical training device.

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3. Fig. 2. Examples of EMG activity of muscles of one of the subjects during motor imagery of walking under conditions of neurointerface control without (а) and with (б) activation of the mechanical training device. By abscissa: time. By ordinate: first four signals – EMG activity of muscles of the right leg (RL); next four signals – of the left leg (LL); below – trajectory of movements during mechanotherapy in the ankle joint (thin line), in the knee joint (bold line) RL and LL; at the bottom – instructions to the subject (bold line: low step – remain at rest, medium step – motor imagery of walking with LL, high step – motor imagery of walking with RL); current value of accuracy of brain signal classification (thin line).

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4. Fig. 3. Individual characteristics of EMG activity of all studied muscles of the lower extremities during motor imagery using a neurointerface with and without activation of the mechanical training device. Rows: subjects, columns: muscles. Black rectangles indicate the presence of burst activity, light gray rectangles indicate tonic activity, dark gray rectangles indicate both burst and tonic activity. Bottom row: number of subjects who had any type of EMG activity (burst and/or tonic) in the corresponding muscle.

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5. Fig. 4. Average EMG activity of the muscles (μV) of the tibialis anterior, gastrocnemius, vastus lateralis and biceps femoris on the right leg (ПН) and left one at rest (gray columns) and during motor imagery of walking beginning from ПН (black columns) or from ЛН (white columns) when controlling the neurointerface with and without activation of the mechanical training device. The asterisk (*) indicates differences between the EMG activity values that are significant when introducing the Bonferroni correction; in the case of a tendency towards significance, the p value is indicated.

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6. Fig. 5. Pleiades reflecting the results of factor and correlation (by Spearman criterion) analysis of the average EMG activity of the tibialis anterior (TA), gastrocnemius (Gs), vastus lateralis (VL) and biceps femoris (BF) muscles of the right (R) and left (L) legs during control of the neurointerface based on motor imagery of walking, beginning with the left or right leg, with and without activation of the mechanical training device. The muscle names in bold are included in the 1st factor determined by factor analysis, in italics – in the 2nd factor. Black solid lines reflect significant correlations between the EMG activity of the muscles included in the 1st factor, black dotted lines – included in the 2nd factor, gray dashed-dotted lines – not included in any of the components. The thickness of the lines reflects the value of the correlation coefficient (the higher it is, the thicker the line).

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