This article presents a rehabilitation technique based on a lower-limb exoskeleton integrated with a human-machine interface (HMI). HMI is used to record and process multimodal signals collected using a foot motor imagery (MI)-based brain-machine interface (BMI) and multichannel electromyographic (EMG) signals recorded from leg muscles. Current solutions of HMI-equipped rehabilitation assistive technologies tested under laboratory conditions demonstrated a great deal of success, but faced several difficulties caused by the limited accuracy of detecting MI electroencephalography (EEG) and the reliability of online control when executing a movement by patients dressed in an exoskeleton. In the case of lowerlimb representation, there is still the problem of reliably distinguishing leg movement intentions and differentiating them in BMI systems. Targeting the design of a rehabilitation technique replicating the natural mode of motor control in exoskeleton walking patients, we have shown how the combined use of multimodal signals can improve the accuracy, performance, and reliability of HMI. The system was tested on healthy subjects operating the exoskeleton under different conditions. The study also resulted in algorithms of multimodal HMI data collection, processing, and classification. The developed system can analyze up to 15 signals simultaneously in real-time during a movement. Foot MI is extracted from EEG signals (seven channels) using the event-related (de)synchronization effect. Supplemented by EMG signals reflecting motor intention, the control system can initiate and differentiate the movement of the right and left legs with a high degree of reliability. The classification and control system permits one to work online when the exoskeleton is executing a movement.

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In this study, we address the issue of whether vibrotactile feedback can enhance the motor cortex excitability translated into the plastic changes in local cortical areas during motor imagery (MI) BCI-based training. For this purpose, we focused on two of the most notable neurophysiological effects of MIthe event-related desynchronization (ERD) level and the increase in cortical excitability assessed with navigated transcranial magnetic stimulation (nTMS). For TMS navigation, we used individual high-resolution 3D brain MRIs. Ten BCI-naive and healthy adults participated in this study. The MI (rest or left/right hand imagery using Graz-BCI paradigm) tasks were performed separately in the presence and absence of feedback. To investigate how much the presence/absence of vibrotactile feedback in MI BCI-based training could contribute to the sensorimotor cortical activations, we compared the MEPs amplitude during MI after training with and without feedback. In addition, the ERD levels during MI BCI-based training were investigated. Our findings provide evidence that applying vibrotactile feedback during MI training leads to (i) an enhancement of the desynchronization level of mu-rhythm EEG patterns over the contralateral motor cortex area corresponding to the MI of the non-dominant hand; (ii) an increase in motor cortical excitability in hand muscle representation corresponding to a muscle engaged by the MI. Index Terms-Brain-computer interfaces (BCI), vibrotactile feedback, motor imagery (MI), event-related desynchronization (ERD), motor cortical excitability, navigated transcranial magnetic stimulation (nTMS), motor evoked potentials (MEPs).

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