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

Lower-limb robotic exoskeletons are wearable devices that can be beneficial for people with lower-extremity motor impairment because they valuable in rehabilitation or assistance. These controlled mentally by means of brain–machine interfaces (BMI). The aim the present study was design a BMI based on imagery (MI) to control gait lower-limb exoskeleton. evaluation is carried out able-bodied subjects as preliminary since potential users limitations. proposed works state machine, i.e., decoding algorithm different start (standing still) and stop (walking). combines two paradigms reducing false triggering rate (when identifies irrelevant brain tasks MI), one another attention user. Research divided into parts. First, during training phase, results showed an average accuracy 68.44 ± 8.46% MI paradigm 65.45 5.53% paradigm. Then, test exoskeleton performance 64.50 10.66%, very few positives. Participants completed various sessions there significant improvement over time. indicate that, after several sessions, developed system may employed controlling exoskeleton, which could benefit assistance device and/or therapeutic approach limited activations.