基于特征融合神经网络的运动想象脑电分类算法

运动想象-脑机接口（MI-BCI）技术为运动障碍患者提供了一种新的与外界交流的能力。应用卷积神经网络（CNN）处理运动想象（MI）脑电分类问题时，多提取最后卷积层的特征，忽视了中间层大量可用信息，导致MI-BCI的分类性能较差。针对这一问题，提出模型内层融合（WMFF）和模型间层融合（CMFF）两种特征融合策略。WMFF策略提取CNN每一层特征进行融合；CMFF策略融合CNN和长短时记忆网络并提取每一层特征。本研究用BCI竞赛IV Datasets 2a数据集对所提方法进行验证，WMFF和CMFF MI脑电信号四分类平均正确率分别达到76.19%和80.46%。结果表明，所提方法可有效提高MI脑电信号分类正确率，为MI脑电信号分类提供了新的思路。

Motor imagery EEG classification algorithm based on feature fusion neural network

Abstract: Motor imagery-based brain computer interface (MI-BCI) technology enables patients with movement disorders to acquire a new ability to communicate with the outside world. However, when using convolutional neural network (CNN) for MI electroencephalogram (EEG) classification, researchers often extract the features of the final convolutional layer and ignore the large amount of available information in the middle layer, resulting in poor classification performance of MI-BCI. To solve this problem, two kinds of feature fusion strategies, namely with-in model fusion-feature (WMFF) and cross model fusion-feature (CMFF), are proposed. WMFF strategy extracts the features of each CNN layer separately for feature fusion while CMFF strategy integrates CNN and long short-term memory network and extracts the features of each layer. BCI competition IV Datasets 2a is used to verify the proposed method, and the results show that the average accuracies of WMFF and CMFF for 4-category MI EEG classification reach 76.19% and 80.46%, respectively, which indicates that the proposed method can effectively improve the accuracy of MI EEG classification, and provide new ideas and methods for the application of MI-BCI.