基于最优子段深度学习的手指运动想象脑电信号分类研究  

Finger motor imagery based on optimal sub-segment deep learning research on EEG signal classification

作　　者：周鹏 叶庆卫[1] 罗慧艳 陆志华 Zhou Peng;Ye Qingwei;Luo Huiyan;Lu Zhihua(Faculty of Electrical Engineering&Computer Science,Ningbo University,Ningbo Zhejiang 315211,China)

机构地区：[1]宁波大学信息科学与工程学院,浙江宁波315211

出　　处：《计算机应用研究》2023年第6期1728-1733,共6页Application Research of Computers

基　　金：国家自然科学基金资助项目(61071198,51675286)。

摘　　要：目前已有的手指运动想象脑电信号多分类任务的分类性能均难以达到可用性能。在详细分析脑电信号时间尺度上的多种成分的基础上,设计一种信号子段提取的自监督子网络,然后把子段输入下一个子网络用于信号分类,两个子网综合成一个自监督混合的多任务深度网络。在训练阶段,子段提取子网络针对每条脑电信号提取不同的子段,由后面的分类子网络来判断该子段是否最佳而自动调整子段位置,总体损失函数由两个子网络的两个损失函数加权而成,通过整体网络学习算法实现最佳子段信号的提取并获得最佳分类效果。验证和测试阶段,子段提取子网络按照训练完成的参数自动提取相应的子段输入分类子网络进行分类。在the largest SCP data of Motor-Imagery和BCI Competition IV中Data sets 4数据集上进行网络性能验证,SCP数据集上全部受试者3指分类任务的平均测试分类准确率达70%以上,4指平均测试分类准确率达60%左右,5指平均测试分类准确率达50%左右,比现有的报道有明显的提升。证实该网络能够有效地提取出运动想象脑电信号子段,具有良好的分类效果和泛化性能。The classification performance of the existing multi-classification tasks of finger motor imagery EEG signals is difficult to achieve usable performance.On the basis of detailed analysis of multiple components on the time scale of EEG signals,this paper designed a self-supervised sub-network for signal sub-segment extraction,and then input the sub-segment into the next sub-network for signal classification,it synthesized the two sub-networks into a self-supervised hybrid multi-task deep network.In the training phase,the sub-segment extraction sub-network extracted different sub-segments for each EEG signal,and the subsequent classification sub-network judged whether the sub-segment was the best and automatically adjusted the position of the sub-segment.It weighted the total loss function by two loss functions of two sub-networks,and extracted the best sub-segment signal and obtained the best classification effect through the overall network learning algorithm.In the verification and testing phase,the sub-segment extraction sub-network automatically extracted the corresponding sub-segment input classification sub-network according to the parameters of the training for classification.It verified the network performance on the largest SCP data of Motor-Imagery data set and the Data sets 4 of BCI Competition IV.On the SCP dataset,the average test classification accuracy of all subjects’three finger classification tasks is more than 70%,the average test classification accuracy of four finger classification tasks is about 60%,and the average test classification accuracy of five finger classification tasks is about 50%,which is significantly improved compared with the existing reports.It is confirmed that the network can effectively extract sub-segments of motor imagery EEG signals,and has good classification effect and generalization performance.

关 键 词：脑电信号 运动想象 深度学习 最优子段 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]