Gu Lingyun, Yu Zhenhua, Ma Tian, Wang Haixian, Li Zhanli, Fan Hui
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:506-509. doi: 10.1109/EMBC44109.2020.9176442.
We use random matrix theory (RMT) to investigate the statistical properties of brain functional networks in lower limb motor imagery. Functional connectivity was calculated by Pearson correlation coefficient (PCC), mutual information (MTI) and phase locking value (PLV) extracted from EEG signals. We found that when the measured subjects imagined the movements of their lower limbs the spectral density as well as the level spacings displayed deviations from the random matrix prediction. In particular, a significant difference between the left and right foot imaginary movements was observed in the maximum eigenvalue from the PCC, which can provide a theoretical basis for further study on the classification of unilateral movement of lower limbs.
我们使用随机矩阵理论（RMT）来研究下肢运动想象中脑功能网络的统计特性。通过从脑电图（EEG）信号中提取的皮尔逊相关系数（PCC）、互信息（MTI）和锁相值（PLV）来计算功能连接性。我们发现，当被测受试者想象其下肢运动时，频谱密度以及能级间距显示出与随机矩阵预测的偏差。特别是，在PCC的最大特征值中观察到左右脚想象运动之间存在显著差异，这可为进一步研究下肢单侧运动的分类提供理论依据。
