Gu Lingyun, Yu Zhenhua, Ma Tian, Wang Haixian, Li Zhanli, Fan Hui
Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, PR China.
College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, Shanxi, PR China.
Neuroscience. 2020 Jun 1;436:93-109. doi: 10.1016/j.neuroscience.2020.04.006. Epub 2020 Apr 10.
This study aims to investigate the difference in cortical signal characteristics between the left and right foot imaginary movements and to improve the classification accuracy of the experimental tasks. Raw signals were gathered from 64-channel scalp electroencephalograms of 11 healthy participants. Firstly, the cortical source model was defined with 62 regions of interest over the sensorimotor cortex (nine Brodmann areas). Secondly, functional connectivity was calculated by phase lock value for α and β rhythm networks. Thirdly, network-based statistics were applied to identify whether there existed stable and significant subnetworks that formed between the two types of motor imagery tasks. Meanwhile, ten graph theory indices were investigated for each network by t-test to determine statistical significance between tasks. Finally, sparse multinomial logistic regression (SMLR)-support vector machine (SVM), as a feature selection and classification model, was used to analyze the graph theory features. The specific time-frequency (α event-related desynchronization and β event-related synchronization) difference network between the two tasks was congregated at the midline and demonstrated significant connections in the premotor areas and primary somatosensory cortex. A few of statistically significant differences in the network properties were observed between tasks in the α and β rhythm. The SMLR-SVM classification model achieved fair discrimination accuracy between imaginary movements of the two feet (maximum 75% accuracy rate in single-trial analyses). This study reveals the network mechanism of the discrimination of the left and right foot motor imagery, which can provide a novel avenue for the BCI system by unilateral lower limb motor imagery.
本研究旨在探究左右脚想象运动之间皮质信号特征的差异，并提高实验任务的分类准确率。从11名健康参与者的64通道头皮脑电图中收集原始信号。首先，在感觉运动皮层（九个布罗德曼区域）上用62个感兴趣区域定义皮质源模型。其次，通过α和β节律网络的锁相值计算功能连接性。第三，应用基于网络的统计方法来识别在两种运动想象任务之间是否存在稳定且显著的子网络。同时，通过t检验对每个网络研究十个图论指标，以确定任务之间的统计显著性。最后，使用稀疏多项式逻辑回归（SMLR）-支持向量机（SVM）作为特征选择和分类模型来分析图论特征。两个任务之间的特定时频（α事件相关去同步化和β事件相关同步化）差异网络集中在中线，并在前运动区和初级体感皮层显示出显著连接。在α和β节律的任务之间观察到一些网络属性的统计学显著差异。SMLR-SVM分类模型在两只脚的想象运动之间实现了较好的区分准确率（单次试验分析中最高准确率为75%）。本研究揭示了左右脚运动想象辨别能力的网络机制，可为基于单侧下肢运动想象的脑机接口系统提供一条新途径。
