A BMI Based on Motor Imagery and Attention for Commanding a Lower-Limb Robotic Exoskeleton: A Case Study

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.

Brain Symmetry Analysis during the Use of a BCI Based on Motor Imagery for the Control of a Lower-Limb Exoskeleton

“…Although all subjects performed eleven trials per session in full-motion and eleven in full-static, some of them were not considered for the analysis. Performing MI while wearing an exoskeleton is a complex task, so it is easy for users to get distracted [16]. Therefore, those trials statistically considered as outliers were removed.…”

“…The experimental design was the same presented in [16]. Each subject performed 5 sessions, which were divided in training and testing.…”

“…The classifier employed was a Linear Discriminant Analysis (LDA) [34]. It has been commonly used on previous investigations [16,35,36] due to its high reported precision and short computational time [37,38]. Consequently, all fullstatic trials for each subject and experimental session were considered for cross-validation.…”

“…Another limitation lies in the fact that performing maintained gait imagery while wearing an exoskeleton can be difficult. Participants can suffer from fatigue, and it is more difficult to focus on the mental tasks, which usually makes for lower BCI accuracies [16].…”

“…There are different BCI control paradigms for this kind of devices. The most commonly used are based on external stimuli such as steady-state visually evoked potentials [7] or auditory evoked potentials [8], and those related with real motion or MI such as motionrelated cortical potentials [9][10][11] or ERD/ERS [12][13][14][15][16]. Controlling a BCI with MI can emulate the lost motor function by inducing mechanisms of neuroplasticity [17].…”