【摘要】 目的为了研究运动想象过程中脑功能网络(brain functional network, BFN)的时频变化特征及对运动想象任务识别的影响,本文提出一种个性化皮尔逊相关系数(personalized Pearson correlation coefficient, PPCC)并用于动态BFN的构建与特征提取方法。方法首先,对各受试者运动想象脑电(motor imagery EEG, MI-EEG)频带范围进行两级筛选,获得其最优频带;然后,将运动想象时间段进行分割,计算各子时段最优频带MI-EEG的PPCC,并用于构建个性化的动态BFN;进而,计算各个BFN的度作为网络特征,并将多时段的网络特征串行融合获得特征向量;最后,针对BCI Competition III Data Set IIIa和BCI Competition IV Data Set 2a数据集,采用支持向量机检验特征的有效性。结果在两个公共数据集上,本文方法的10×10折交叉验证最高识别率分别为100.00%和68.84%。与基于共空间模式和基于PCC的BFN特征提取方法相比,具有明显的优势,双样本t检验的结果也充分表明了PPCC的优越性。结论与PCC相比,基于PPCC能构建出可客观地展现运动想象个性化特点的动态BFN,反映了不同受试者运动想象时大脑激活的差异性,及其在时域和频域同时呈现的动态变化特点,有效增强了特征提取的自适应性。更多还原

【Abstract】 Objective In order to study the time-frequency variation characteristics of brain functional network(BFN) in the process of motor imagery(MI) and the impact on the recognition of MI tasks, this paper proposes a personalized Pearson correlation coefficient(PPCC),which is applied to dynamic BFN construction and feature extraction.Methods First, two-stage screening is performed on each subject’s motor imagery EEG(MI-EEG) frequency band range to obtain the optimum frequency band. Then,the MI time period is segmented into several sub-periods,the PPCC of the optimum frequency band MI-EEG is calculated for each sub-period, and personalized dynamic BFNs are constructed based on the PPCC values. Furthermore, the degree of each BFN is calculated as network features, which are serially fused to obtain feature vectors. Finally,the feature vectors are evaluated by support vector machine on the BCI Competition III Data Set IIIa and BCI Competition IV Data Set 2 a.Results On the two public datasets, the 10×10 fold highest recognition rates achieve 100.00% and 68.84%, which have obvious advantages compared with common spatial pattern-based and PCC-based BFN feature extraction methods.The two-sample t-test clearly shows its superiority as well.Conclusions Compared with PCC, the dynamic BFNs, which objectively express the personalized characteristic of MI-EEG, can be constructed based on PPCC, reflect the differences of activated brain areas caused by MI among different subjects and the characteristics of dynamic changes exhibiting in time and frequency domains simultaneously, and effectively enhance the adaptivity of feature extraction.更多还原