Abstract
Lower-limb robotic exoskeletons are wearable devices that can be beneficial for people with lower-extremity motor impairment because they can be valuable in rehabilitation or assistance. These devices can be controlled mentally by means of brain–machine interfaces (BMI). The aim of the present study was the design of a BMI based on motor imagery (MI) to control the gait of a lower-limb exoskeleton. The evaluation is carried out with able-bodied subjects as a preliminary study since potential users are people with motor limitations. The proposed control works as a state machine, i.e., the decoding algorithm is different to start (standing still) and to stop (walking). The BMI combines two different paradigms for reducing the false triggering rate (when the BMI identifies irrelevant brain tasks as MI), one based on motor imagery and another one based on the attention to the gait of the user. Research was divided into two parts. First, during the training phase, results showed an average accuracy of 68.44 ± 8.46% for the MI paradigm and 65.45 ± 5.53% for the attention paradigm. Then, during the test phase, the exoskeleton was controlled by the BMI and the average performance was 64.50 ± 10.66%, with very few false positives. Participants completed various sessions and there was a significant improvement over time. These results indicate that, after several sessions, the developed system may be employed for controlling a lower-limb exoskeleton, which could benefit people with motor impairment as an assistance device and/or as a therapeutic approach with very limited false activations.
Highlights
Robotic exoskeletons are wearable devices that can enhance physical performance and provide movement assistance
Contrary to the findings of our previous work on a brain–machine interfaces (BMI)-controlled treadmill [19], we found significant differences between opened-loop trials in which subjects were standing and when they were walking
The current research presents a BMI system based on motor imagery (MI) and attention paradigms that has been tested to control a lower-limb exoskeleton
Summary
Robotic exoskeletons are wearable devices that can enhance physical performance and provide movement assistance. The combination of lower-limb robotic exoskeletons with brain–machine interfaces (BMI), which are systems that decode neural activity to drive output devices, offers a new method to provide motor support. There are different BMI control paradigms for lower-limb exoskeletons based on brain changes. The most common ones are steady-state visually evoked potentials [2], which are based on visual stimuli; motion-related cortical potentials [3,4,5,6], which are produced between 1500 and 500 ms before the execution of the movement, and and event-related desynchronization/synchronization (ERD/ERS), which is considered to indicate the activation and posterior recovery of the motor cortex during preparation and completion of a movement [7,8,9]. The work of [16] combined MI with eye blinks as a control criterion
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