A New EEG-based Paradigm for Classifying Intention of Compound-Limbs Movement

Traditional lower limb exoskeleton robots utilize electromechanical control panels or buttons to assist patients with physical disabilities, which is a passive training way of rehabilitation. Over the past few years, extensive research has been conducted on brain-controlled lower limb exoskeleton robot technology combined with an electroencephalogram (EEG) signals. However, the way most paradigms are designed does not conform to the natural walking posture of human beings. In this study, a new EEG-based paradigm is proposed for detecting the intention of compound-limbs movement, which is closer to human walking posture. The time-frequency analysis presents that there showed stronger event-related desynchronization (ERD) at the main channels. Besides, the brain topographical distribution shows that the ERD not only exists in the contralateral sensorimotor area, but also appears on the central parietal lobe region (the leg motion mapping region), which initially verified the possibility of differentiating this pattern. Then, after extracting time-frequency-spatial features by common spatial pattern method, three supervised machine learning algorithms are used to classify the compound limb movement. The results demonstrate that the classification performance of compound-limbs movement mode are much higher than that of single-leg movement (>20%). This research introduces a new paradigm for classifying lower-limbs related movement intention, which might help control the lower limbs exoskeleton with subjects’ voluntary intention and improve the effect of human-machine interface system.