Classification of emotional brain networks based on weighted <i>K</i>-order propagation number_Journal of Biomedical Engineering

Authors：

QIAN Yutong , SHEN Jian , ZHANG Jiazhen , HE Tanqin ,  HUANG Liya

National and Local Joint Engineering Laboratory of RF Integration and Micro-Assembly Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, P.R.China;

Corresponding author：

HUANG Liya, Email: huangly@njupt.edu.cn

Keywords：

brain network; weighted K-order propagation number; importance of nodes; feature extraction; support vector machine

DOI：

10.7507/1001-5515.201905039

Video：

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Abstract Full text Figures/Tables Video References Cited by

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 K-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 K-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.

Citation： QIAN Yutong, SHEN Jian, ZHANG Jiazhen, HE Tanqin, HUANG Liya. Classification of emotional brain networks based on weighted K-order propagation number. Journal of Biomedical Engineering, 2020, 37(3): 412-418. doi: 10.7507/1001-5515.201905039 Copy

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1. Mohammadpour M, Hashemi S M R, Houshmand N. Classification of EEG-based emotion for BCI applications//2017 Artificial Intelligence and Robotics (IRANOPEN). Qazvin: IEEE, 2017: 127-131.
2. Candra H, Yuwono M, Chai R, et al. Investigation of window size in classification of EEG-emotion signal with wavelet entropy and support vector machine//The 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Milan: IEEE, 2015: 1-4.
3. Tripathi S, Acharya S, Sharma R D, et al. Using deep and convolutional neural networks for accurate emotion classification on DEAP dataset//Proceedings of the Twenty-Ninth AAAI Conference on Innovative Applications. Palo Alto: AAAI Press, 2017: 4746-4752.
4. Wang N, Wang Y, Li Y, et al. Gamma oscillation in brain connectivity in emotion recognition by Granger causality//2011 4th International Conference on Biomedical Engineering and Informatics (BMEI). Shanghai: IEEE, 2011: 762-766.
5. Richman J S, Moorman J R. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol, 2000, 278(6): H2039-H2049.
6. Pincus S M, Viscarello R R. Approximate entropy: a regularity measure for fetal heart rate analysis. Obstet Gynecol, 1992, 79(2): 249-255.
7. Ledoux J E. Emotion circuits in the brain. Annu Rev Neurosci, 2009, 23(23): 155-184.
8. 汪小帆, 李翔, 陈关荣. 复杂网络理论及其应用. 北京: 清华大学出版社, 2006.
9. Kitsak M, Gallos L K, Havlin S, et al. Identification of influential spreaders in complex networks. Nat Phys, 2010, 6(11): 888-893.
10. Berkhin P. A survey on pagerank computing. Internet Math, 2005, 2(1): 73-120.
11. 黄丽亚, 霍宥良, 王青, 等. 基于K-阶结构熵的网络异构性研究. 物理学报, 2019, 68(1): 325-336.
12. 黄丽亚, 汤平川, 霍宥良, 等. 基于加权K-阶传播数的节点重要性研究. 物理学报, 2019, 68(12): 311-321.
13. Diego Rodriguez J, Perez A, Antonio Lozano J. Sensitivity analysis of k-fold cross validation in prediction error estimation. IEEE Trans Pattern Anal Mach Intell, 2010, 32(3): 569-575.
14. Koelstra S, Muhl C, Soleymani M, et al. DEAP: A database for emotion analysis using physiological signals. IEEE Trans Affect Comput, 2012, 3(1): 18-31.
15. Russell J A, Lewicka M, Niit T. A cross-cultural study of a circumplex model of affect. J Pers Soc Psychol, 1989, 57(5): 848-856.

Journal of Biomedical Engineering

Classification of emotional brain networks based on weighted K-order propagation number

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