Abstract:
: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.
journal_name
Comput Intell Neuroscijournal_title
Computational intelligence and neuroscienceauthors
Gao J,Wang W,Zhang Jdoi
10.1155/2016/6184823subject
Has Abstractpub_date
2016-01-01 00:00:00pages
6184823eissn
1687-5265issn
1687-5273journal_volume
2016pub_type
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