Experimental Protocol to Assess Neuromuscular Plasticity Induced by an Exoskeleton Training Session

Abstract

Exoskeleton gait rehabilitation is an emerging area of research, with potential applications in the elderly and in people with central nervous system lesions, e.g., stroke, traumatic brain/spinal cord injury. However, adaptability of such technologies to the user is still an unmet goal. Despite important technological advances, these robotic systems still lack the fine tuning necessary to adapt to the physiological modification of the user and are not yet capable of a proper human-machine interaction. Interfaces based on physiological signals, e.g., recorded by electroencephalography (EEG) and/or electromyography (EMG), could contribute to solving this technological challenge. This protocol aims to: (1) quantify neuro-muscular plasticity induced by a single training session with a robotic exoskeleton on post-stroke people and on a group of age and sex-matched controls; (2) test the feasibility of predicting lower limb motor trajectory from physiological signals for future use as control signal for the robot. An active exoskeleton that can be set in full mode (i.e., the robot fully replaces and drives the user motion), adaptive mode (i.e., assistance to the user can be tuned according to his/her needs), and free mode (i.e., the robot completely follows the user movements) will be used. Participants will undergo a preparation session, i.e., EMG sensors and EEG cap placement and inertial sensors attachment to measure, respectively, muscular and cortical activity, and motion. They will then be asked to walk in a 15 m corridor: (i) self-paced without the exoskeleton (pre-training session); (ii) wearing the exoskeleton and walking with the three modes of use; (iii) self-paced without the exoskeleton (post-training session). From this dataset, we will: (1) quantitatively estimate short-term neuroplasticity of brain connectivity in chronic stroke survivors after a single session of gait training; (2) compare muscle activation patterns during exoskeleton-gait between stroke survivors and age and sex-matched controls; and (3) perform a feasibility analysis on the use of physiological signals to decode gait intentions.

Keywords: EEG; EMG; aging; exoskeleton; neuromuscular plasticity; rehabilitation; stroke.

Figure 1

Participant preparation: (a) anthropometric measurements for EKSO customisation; (b) minimal crosstalk area recognition for EMG sensors placement; (c) EEG cap, EMG electrodes and probes and IMUs placement; (d) example of EKSO walking.

Figure 2

Walking trials: (a) Free walking; and (b) EKSO walking.

Figure 3

Example of filtered (in [1–40] Hz) EEG time series pre- (first column) and post- (second column) training with EKSO in a control subject. In the first row, it is reported 8 s of the EEG time-series. In the second row, it is displayed the power spectral density (PSD) computed between 1 and 40 Hz of the signals reported above. PSD was computed for the EEG signal recorded in each electrode using Welch’s 50% overlapped 2-s segment averaging estimator. For each EEG frequency band (i.e., Delta (1–4) Hz), Theta (4–8) Hz, Alpha (8–12) Hz, Beta (12–24) Hz), it is also reported the topographic map of the power spectra.

Figure 4

Example of filtered EMG time series during free-walking pre-training and free-walking post-training with the EKSO. Vertical lines correspond to foot-strike (solid lines) and foot-off (dashed) gait events. Red lines highlight the events for the left side, whereas the right side events are reported in green.