基于脑功能网络和共空间模式分析脑电情绪识别

传统脑网络的情绪分类将聚类系数、平均最短路径等拓扑属性作为分类特征。针对这些属性易受网络连接阈值和特征选择的影响,难以完全表征不同情绪状态下的网络空间拓扑结构差异的问题,提出了一种基于脑网络和共空间模式的脑电情绪识别方法(EEG emotion classification based on common spatial patterns of brain networks topology,EEC-CSP-BNT)。该算法基于互信息在各个子频段内计算电极间的功能连接矩阵,同时利用共空间模式(common spatial pattern,CSP)分析学习空间滤波器,构建分类特征,最后通过分类器(如Fisher线性判别、支持向量机、K最近邻)实现基于脑电的情绪分类。基于DEAP和SEED数据集的实验结果表明,相比于脑网络拓扑属性,EEC-CSP-BNT能有效提取脑网络拓扑结构的分类信息,提高脑电情绪识别性能。

EEG emotion recognition based on common spatial pattern of brain functional network

The emotion classification of traditional brain network uses clustering coefficients,average shortest path and other topological attributes as classification features.To solve the problem that EEG emotion recognition based on these attributes is susceptible to network connectivity thresholds and attribute selection,and network topology attributes are difficult in fully cha-racterizing the differences in network structure for different emotional states,this paper proposed an EEG emotion classification based on common spatial patterns of brain networks topology(EEC-CSP-BNT).EEC-CSP-BNT calculated the functional connectivity matrix in the sensor space using mutual information for each sub-band,and employed the CSP to learn the spatial filters and constructed classification features.At last,it employed the pattern classifiers(such as Fisher linear discrimination,support vector machine and K nearest neighbor)to complete the emotion recognition.Experimental results using DEAP and SEED datasets validate the superior performance of EEC-CSP-BNT compared to the network topology attributes features.EEC-CSP-BNT can also extract the useful classification information of brain network topology.