基于改进深度森林的运动想象脑电分类方法  

Classification method of motor imagery EEG based on improved deep forest

作　　者：李红利[1] 郭伟[1] 张荣华[2] 修春波[1] 马欣 LI Hong-li;GUO Wei;ZHANG Rong-hua;XIU Chun-bo;MA Xin(School of Control Science and Engineering,Tiangong University,Tianjin 300387,China;School of Artificial Intelligence,Tiangong University,Tianjin 300387,China;School of Electronic and Information Engineering,Tiangong University,Tianjin 300387,China)

机构地区：[1]天津工业大学控制科学与工程学院,天津300387 [2]天津工业大学人工智能学院,天津300387 [3]天津工业大学电子与信息工程学院,天津300387

出　　处：《控制与决策》2023年第2期468-474,共7页Control and Decision

基　　金：国家自然科学基金项目(62071328);天津市技术创新引导专项基金项目(21YDTPJC00540,21YDTPJC00550)。

摘　　要：基于运动想象的脑电信号是用户在执行不同运动想象任务时采集到的不同脑区的电信号.受到用户的大脑结构和头皮状态等因素影响,采集到的运动想象任务信号之间混乱,从而导致大量信号被错分.鉴于此,提出一种基于改进深度森林的运动想象任务信号分类方法.首先,利用变长粒子群算法强大的寻优能力,为深度森林中每一层的随机森林和完全随机森林预测的类概率值搜寻最优权重;然后,将此权重赋予对应的类概率值,以此实现对结果修正目的;最后,利用BCI竞赛IV的数据集2 a评估所提出方法的有效性.实验结果表明,相比传统的深度森林,该方法对四分类运动想象脑电信号实现了更高的分类准确率.所提出方法根据分类器预测的结果进行学习,对于提升分类器性能的研究具有重要意义.EEG signals based on motor imagery are electrical signals acquired from different brain regions when subjects perform different motor imagery tasks.Influenced by the factors such as the difference of brain structures and scalp states of different subjects,the acquired signals of different motor imagery tasks are usually confused,which will result in the misclassifications for a large number of motor imagery signals.A classification algorithm for motor imagery tasks based on improved deep forest is proposed.Firstly,the powerful optimization ability of the variable-length particle swarm algorithm is adopted to search for the optimal weights for probability values of each class predicted by the random forest and completely random forest in the deep forest.Secondly,the weights are assigned to the corresponding class probability values,so as to realize the purpose of the result correction.Finally,the data set 2a of BCI competition IV is adopted to evaluate the efficacy of the proposed algorithm.The results show that the proposed algorithm achieves a higher classification accuracy for the four classes of motor imagery signals in comparison with the traditional deep forest.The proposed algorithm can learn from the predicted results of the classifier,which is of great significance for the improvement of the classifier performance.

关 键 词：运动想象 深度森林 变长粒子群优化 修正策略 分类识别 

分 类 号：TH79[机械工程—仪器科学与技术] TN911.7[机械工程—精密仪器及机械] R318[电子电信—通信与信息系统]