Gao Jia, Wang Wei, Zhang Ji
Laboratory of Machine Learning and Cognition, Nanjing Normal University, Nanjing 210097, China.
Faculty of Health, Engineering and Sciences, University of Southern Queensland, Toowoomba, QLD 4350, Australia.
Comput Intell Neurosci. 2016;2016:6184823. doi: 10.1155/2016/6184823. Epub 2016 Jan 10.
This paper investigated the interregional correlation changed by sport training through electroencephalography (EEG) signals using the techniques of classification and feature selection. The EEG data are obtained from students with long-time professional sport training and normal students without sport training as baseline. Every channel of the 19-channel EEG signals is considered as a node in the brain network and Pearson Correlation Coefficients are calculated between every two nodes as the new features of EEG signals. Then, the Partial Least Square (PLS) is used to select the top 10 most varied features and Pearson Correlation Coefficients of selected features are compared to show the difference of two groups. Result shows that the classification accuracy of two groups is improved from 88.13% by the method using measurement of EEG overall energy to 97.19% by the method using EEG correlation measurement. Furthermore, the features selected reveal that the most important interregional EEG correlation changed by training is the correlation between left inferior frontal and left middle temporal with a decreased value.
本文利用分类和特征选择技术，通过脑电图（EEG）信号研究了体育训练对区域间相关性的影响。EEG数据取自长期接受专业体育训练的学生以及未经体育训练的正常学生作为基线。19通道EEG信号的每个通道都被视为脑网络中的一个节点，并计算每两个节点之间的皮尔逊相关系数作为EEG信号的新特征。然后，使用偏最小二乘法（PLS）选择变化最大的前10个特征，并比较所选特征的皮尔逊相关系数以显示两组之间的差异。结果表明，两组的分类准确率从使用EEG总能量测量方法的88.13%提高到使用EEG相关性测量方法的97.19%。此外，所选特征表明，训练导致的最重要的区域间EEG相关性变化是左下额叶和左中颞叶之间的相关性降低。
