The motor-based brain–computer interface is widely used in the exoskeleton reconstruction of patients with muscle weakness and to enhance the operating experience of somatosensory game customers through the combination of actions and electroencephalography signals. However, the recognition algorithms in traditional motor-based brain–computer interfaces have problems such as “brain–computer interface blindness” (recognition accuracy is less than 70%) and “one person one model.” In this study, a regularized long short-term memory algorithm and a hardware platform for gesture recognition by using the motor-based brain–computer interface are proposed. Experimental results show that the gesture recognition accuracy rate based on the motor brain–computer interface is up to 95.69%, which is significantly better than that of other algorithms. The proposed model enhances the applicability and generalization ability of the brain–computer interface, for which the practicability and effectiveness are verified.

基于电机的脑机接口广泛用于肌肉无力患者的外骨骼重建，并通过动作和脑电图信号的结合来增强体感游戏客户的操作体验。但是，传统的基于运动的脑机接口的识别算法存在诸如“脑机接口失明”（识别精度低于70％）和“一个人一个模型”的问题。在这项研究中，提出了一种正则化的长短期记忆算法和使用基于电机的脑机接口进行手势识别的硬件平台。实验结果表明，基于运动脑机接口的手势识别准确率高达95.69％，明显优于其他算法。