Affiliations: [a] Tandon School of Engineering of New York University, New York, USA
| [b] School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
| [c] Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, China
| [d] Institute of Neuroscience, Zhengzhou University, China
Abstract: Movement function rehabilitation of patients with craniocerebral injuries is an important issue facing neurorehabilitation science. The use of brain–computer interface technology in rehabilitation training systems can allow patients to actively participate in the rehabilitation training process and use the brain’s neuroplasticity to enhance the effects from rehabilitation training. At present, the brain–computer interface-based rehabilitation training system still has problems such as insufficient active participation of patients, resulting in slowed motor neural circuit repair or low action execution accuracy. In response to the above problems, this paper designed an active and passive upper limb rehabilitation training system based on a hybrid brain–computer interface of steady-state visual evoked potentials (SSVEP) and movement-related cortical potentials (MRCPs). The system includes six parts: task setting and training guidance module, EEG signal acquisition module, EEG signal preprocessing and recognition module, rehabilitation training module, training completion evaluation module, and communication module. The system drives the rehabilitation robot to complete the training actions by identifying the participant’s SSVEP and evaluates the completion of the rehabilitation training based on the patient’s movement intention recognition results. In this study, 12 participants were recruited. In the online test, the system achieved an average action execution accuracy of 99.3%. The movement intention detection based on MRCPs reached an average accuracy of 82.7%. The participants’ average completion rate was 0.91. The experimental results show that the system can achieve a high rate of execution accuracy. In addition, it can evaluate the active participation level of patients in rehabilitation training based on the movement intention detection results, accelerate the reconstruction of motor neural circuits, improve the effects of training, and provide more effective ways of thinking for the study of upper limb rehabilitation training systems for patients with craniocerebral injuries.
Keywords: EEG, Hybrid-BCI, motor dysfunction, upper limb rehabilitation training, training evaluation
