摘要
脑地形图可以用来可以监测大脑的活动状态,为了准确提取被试大脑活动产生信号的空间特征以及有效提高分类准确率,结合脑地形图和卷积神经网络提出一种多模态脑地形图神经网络分类算法(MBTMNN),对运动想象和心算进行分类识别。对脑电和近红外信号进行预处理,提取脑电的能量特征和近红外中氧合血红蛋白浓度特征,结合各自电极位置统一所有样本的colormap后生成脑地形图,将二者同时输入到卷积神经网络并在特征层进行融合得到训练模型。利用2017年柏林脑电-近红外公开数据集进行六折交叉验证实验,数据集包含29名被试,各300个样本,在运动想象左/右、心算/静息、运动想象/心算/静息和运动想象左/右/心算/静息等4种分类场景中,分别达到了82.91%、94%、90.34%和78.18%的准确率,高于同数据集的近期研究和单模态方法。所提出方法能够有效融合脑电和近红外信号以提高分类精度。
The brain topographic map can be used to monitor the state of brain activity.In order to accurately extract the spatial characteristics of the signals generated by the brain activity of the subjects and effectively improve the classification accuracy,a multi-modal brain topographic map neural network classification algorithm(MBTMNN)was proposed by combining the brain topographic map and convolutional neural network to classify and recognize motor imagery and mental arithmetic.Firstly,EEG and fNIRS signals were preprocessed to extract the energy characteristics of EEG and the concentration characteristics of oxygenated hemoglobin in fNIRS.The colormap of all samples was unified by combining with the position of each electrode to generate brain topographic maps.The EEG and fNIRS signals were simultaneously input into the convolutional neural network and fused in the feature layer to obtain a training model.The six-fold cross validation experiment was conducted on the 2017 Berlin EEG/fNIRS public dataset.The dataset included 29 subjects,with 300 samples each,in the four classification scenarios of left versus right hand motor imagery,mental arithmetic versus resting state,motor imagery versus mental arithmetic versus resting state,and left versus right hand motor imagery versus mental arithmetic versus resting state,the accuracy rates were 82.91%,94%,90.34%and 78.18%,respectively,which were higher than those of recently reported with the same dataset and the state of single mode method.The results indicated that the proposed method effectively fused EEG and fNIRS signals to improve the classification accuracy.
作者
何群
徐香院
江国乾
单伟
童云杰
谢平
He Qun;Xu Xiangyuan;Jiang Guoqian;Shan Wei;Tong Yunjie;Xie Ping(Institute of Electric Engineering,Yanshan University,Qinhuangdao 066004,Hebei,China;Library,Yanshan University,Qinhuangdao 066004,Hebei,China;Department of Biomedical Engineering,Purdue University,Indiana 47907,USA)
出处
《中国生物医学工程学报》
CAS
CSCD
北大核心
2023年第3期301-310,共10页
Chinese Journal of Biomedical Engineering
基金
国家自然科学基金(U20A20192,62076216)
秦皇岛市科学技术研究与发展计划项目(201902A032)。
关键词
脑机接口
多模态
卷积神经网络
脑地形图
brain-computer interface(BCI)
multi-modal
convolutional neural network(CNN)
brain topographic map
