Brain-Controlled Robot Enables the Paraplegic Implement Autonomous Multimode Walk Training

Implementation of the autonomous walk training plays an important role for patients with lower limb paralysis, which however is still an open question presently due to the extreme difficulty of accurately recognizing the patients’ motor intentions in a natural way. In this study, a brain-controlled robot system, mainly consisting of a noninvasive brain–computer interface (BCI) and an elaborately designed lower limb rehabilitation robot, was developed to enable the paralyzed patients to implement the autonomous multimode walk training. First, an enhanced motor imagery based BCI paradigm was designed to improve the subjects’ imagination abilities to generate more separable electroencephalogram (EEG) data. Then, a concept of reaction time was introduced to select the valid EEG samples, and a rhythm combination, consisting of the most complete related sensorimotor rhythms to date, was designed to fully consider their influence. The reaction time, the rhythm combination, and the key parameters of the EEG decoder were collaboratively optimized to realize accurate and robust recognition of the subjects’ motor intentions. Moreover, a human–computer mutual learning based coevolution strategy was proposed, by which the subject and the decoder can be regulated to suit each other to obtain the satisfactory online performance. Finally, the proposed methods were deployed on the brain-controlled robot system, by which multimode walk training can be implemented autonomously. 18 subjects including 9 paraplegic patients were recruited in the experiments, and all of them successfully implemented the autonomous walk training after only about 25 minutes in total for EEG data recording and model training.