Characterizing the onset and offset of motor imagery during passive arm movements to control an upper-body exoskeleton



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In recent decades, two distinct technological advances have been made to understand and improve motor rehabilitation: human-robot interaction and brain-machine interfaces (BMIs). While the introduction of robots has been shown to increase the dosage and intensity of therapy, robot-mediated rehabilitation has failed to achieve clinically relevant levels of improvement over conventional therapy due to its limited influence on neural recovery. On the other hand, BMI-driven therapies have been able to engage neural activity, but the amount and extent of proprioceptive feedback elicited by passive movements were not sufficiently rich to boost activity-dependent plasticity. Harnessing their combined efforts could open up unprecedented opportunities for connecting neural commands to motor output, which may be the missing link to achieving clinically relevant recovery. However, a significant challenge is whether motor intentions from the user can be accurately detected using non-invasive BMIs in the presence of instrumental noise and passive movements induced by the rehabilitation exoskeleton. Therefore, this study aims to characterize the onset and offset of motor imagery during passive arm movements induced by an upper-body exoskeleton to allow for the natural control (initiation and termination) of functional movements. Ten participants were recruited to perform kinesthetic motor imagery (MI) of the right arm while attached to the robot, simultaneously cued with LEDs indicating the initiation and termination of a goal-oriented reaching task. Using electroencephalogram (EEG) signals, we built a decoder to detect the transition between i) rest and beginning MI and ii) maintaining and ending MI. Offline decoder evaluation achieved group average onset accuracy of 60.7% and 66.6% for offset accuracy, revealing that the start and stop of MI could be identified while attached to the robot. Furthermore, pseudo-online evaluation could replicate this performance, simulating reliable online exoskeleton control. Finally, results from a pilot study indicate the feasibility of the onset and offset of MI to control passive arm movements induced by an upper-body exoskeleton using a novel real-time streaming hierarchical machine-learning approach. Our method showed that participants could produce strong and reliable sensorimotor rhythms regardless of noise or passive arm movements induced by wearing the exoskeleton, which opens new possibilities for BMI control of assistive devices and could have further implications for novel neurorehabilitation strategies.


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