Decoding the ERD/ERS: influence of afferent input induced by a leg assistive robot

This paper investigates the influence of the leg afferent input, induced by a leg assistive robot, on the decoding performance of a BMI system. Specifically, it focuses on a decoder based on the event-related (de)synchronization (ERD/ERS) of the sensorimotor area. The EEG experiment, performed with healthy subjects, is structured as a 3 × 2 factorial design, consisting of two factors: “finger tapping task” and “leg condition.” The former is divided into three levels (BMI classes), being left hand finger tapping, right hand finger tapping and no movement (Idle); while the latter is composed by two levels: leg perturbed (Pert) and leg not perturbed (NoPert). Specifically, the subjects' leg was periodically perturbed by an assistive robot in 5 out of 10 sessions of the experiment and not moved in the remaining sessions. The aim of this study is to verify that the decoding performance of the finger tapping task is comparable between the two conditions NoPert and Pert. Accordingly, a classifier is trained to output the class of the finger tapping, given as input the features associated with the ERD/ERS. Individually for each subject, the decoding performance is statistically compared between the NoPert and Pert conditions. Results show that the decoding performance is notably above chance, for all the subjects, under both conditions. Moreover, the statistical comparison do not highlight a significant difference between NoPert and Pert in any subject, which is confirmed by feature visualization.