Classification of emotional brain networks based on weighted -order propagation number.
10.7507/1001-5515.201905039
- Author:
Yutong QIAN
1
;
Jian SHEN
1
;
Jiazhen ZHANG
1
;
Tanqin HE
1
;
Liya HUANG
1
Author Information
1. National and Local Joint Engineering Laboratory of RF Integration and Micro-Assembly Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, P.R.China.
- Publication Type:Journal Article
- Keywords:
brain network;
feature extraction;
importance of nodes;
support vector machine;
weighted K-order propagation number
- From:
Journal of Biomedical Engineering
2020;37(3):412-418
- CountryChina
- Language:Chinese
-
Abstract:
Electroencephalography (EEG) signals are strongly correlated with human emotions. The importance of nodes in the emotional brain network provides an effective means to analyze the emotional brain mechanism. In this paper, a new ranking method of node importance, weighted -order propagation number method, was used to design and implement a classification algorithm for emotional brain networks. Firstly, based on DEAP emotional EEG data, a cross-sample entropy brain network was constructed, and the importance of nodes in positive and negative emotional brain networks was sorted to obtain the feature matrix under multi-threshold scales. Secondly, feature extraction and support vector machine (SVM) were used to classify emotion. The classification accuracy was 83.6%. The results show that it is effective to use the weighted -order propagation number method to extract the importance characteristics of brain network nodes for emotion classification, which provides a new means for feature extraction and analysis of complex networks.
