CNN实现的运动想象脑电分类及人-机器人交互

基于脑电的脑机交互能帮助肢体运动障碍患者进行日常生活和康复训练，但是，由于脑电信号存在信噪比较低、个体差异性大等问题，导致脑电特征的提取与分类还需要进一步提高准确性和效率.因此，在减少脑电采集通道数目、增加分类数目的前提下，基于卷积神经网络对运动想象中的脑电信号进行分类.首先，基于已有方法进行探索实验，建立由3层卷积层、3层池化层和2层全连接层构成的卷积神经网络；然后针对想象左手、右手、脚的运动和静息态设计与开展了实验，获取了相关脑电数据；之后，利用脑电数据训练出基于卷积神经网络的分类模型，测试结果表明，该模型平均分类识别率达到了82.81%，且高于已有的相关分类算法；最后，将已建立的分类模型应用于运动想象信号的在线分类，设计与开发了脑机交互应用原型系统，驱动人-机器人之间的实时交互，帮助用户利用运动想象控制仿人机器人的抬手、前进等运动状态.进一步的测试结果表明，机器人对用户控制命令的平均识别率达到了80.31%，从而验证了所提方法可以对运动想象脑电数据进行较为精确的实时分类，可以促进脑机接口技术在人-机器人交互中的应用.

CNN Based Motor Imagery EEG Classification and Human-robot Interaction

The electroencephalograph (EEG) driven brain-computer interaction can promote daily life and rehabilitation training for physically disabled people, however, EEG has several problems such as low signal-noise ratio, significant individual difference, and these problems result in the low accuracy and efficiency for EEG feature extraction and classification. In the context of reducing numbers of electrodes and increasing identified classes, this study proposed an approach to classify motor imagery (MI) EEG signal based on convolutional neural network (CNN). Firstly, based on existed approaches, experiments were conducted and the CNN was constructed with three convolution layers, three pooling layers, and two full-connection layers. Secondly, MI experiment was conducted with the imagination of left hand movement, right hand movement, foot movement, and resting state, and the MI EEG data were collected at the same time. Thirdly, the MI EEG data set were used to build the classification model based on CNN, and the experiment results indicate that the average accuracy of classification is 82.81%, which is higher than the related classification algorithms. Finally, the classification model was applied in the online classification of MI EEG, and a BCI prototype system was designed and implemented to drive the real-time human-robot interaction. The prototype system can help users to control motion states of the humanoid robot, such as raising hands, moving forward. Furthermore, the experimental results show that the average accuracy of robot controlling reaches to 80.31%, and it verifies the proposed approach not only can classify MI EEG data with high accuracy in real time, but also promote applications of human-robot interaction with BCI.