With the promotion of brain-computer interface technology, it is possible to study brain control system through EEG signals in recent years. In order to solve the problem of EEG signal classification effectively, a motor imagery classification algorithm based on recurrence plot convolution neural network is proposed. Firstly, EEG signals are preprocessed to enhance the signal intensity in the exercise interval. Secondly, time-domain and frequency-domain features are extracted respectively to construct the feature mode of recurrence plot. Finally, a new neural network is established to realize the accurate recognition of left and right movements. This research can also be transferred to other research fields.

随着脑机接口技术的发展，近年来通过脑电信号研究脑控制系统成为可能。为了有效解决脑电信号分类问题，提出了一种基于递归积卷积神经网络的运动图像分类算法。首先，对脑电信号进行预处理，以增强运动间隔中的信号强度。其次，分别提取时域和频域特征，以构造递归图的特征模式。最后，建立了一个新的神经网络，以实现对左右运动的准确识别。该研究也可以转移到其他研究领域。