Research on classification of brain functional network features during mental fatigue_Journal of Biomedical Engineering

Authors：

 YANG Shuo ^1,2 , AI Na ² , WANG Lei ^1,2 , ZHANG Ying ² , XU Guizhi ^1,2

1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P.R.China;
2. Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin 300130, P.R.China;

Corresponding author：

YANG Shuo, Email: sureyang@126.com

Keywords：

mental fatigue; electroencephalogram; brain functional network; support vector machine

DOI：

10.7507/1001-5515.201609032

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This study is aimed to investigate objective indicators of mental fatigue evaluation to improve the accuracy of mental fatigue evaluation. Mental fatigue was induced by a sustained cognitive task. The brain functional networks in two states (normal state and mental fatigue state) were constructed based on electroencephalogram (EEG) data. This study used complex network theory to calculate and analyze nodal characteristics parameters (degree, betweenness centrality, clustering coefficient and average path length of node), and served them as the classification features of support vector machine (SVM). Parameters of the SVM model were optimized by gird search based on 6-fold cross validation. Then, the subjects were classified. The results show that characteristic parameters of node of brain function networks can be divided into normal state and mental fatigue state, which can be used in the objective evaluation of mental fatigue state.

Citation： YANG Shuo, AI Na, WANG Lei, ZHANG Ying, XU Guizhi. Research on classification of brain functional network features during mental fatigue. Journal of Biomedical Engineering, 2018, 35(2): 171-175. doi: 10.7507/1001-5515.201609032 Copy

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1. Tanaka M, Ishii A, Watanabe Y. Neural effect of mental fatigue on physical fatigue: A magnetoencephalography study. Brain Res, 2014, 1542(1): 49-55.
2. 王晓路, 高翔, 许敏鹏, 等. 基于睡眠剥夺模型的脑力疲劳检测方法研究. 生物医学工程学杂志, 2015, 32(3): 497-502.
3. Laurent F, Valderrama M, Besserve M, et al. Multimodal information improves the rapid detection of mental fatigue. Biomed Signal Process Control, 2013, 8(4): 400-408.
4. 万育辰. 脑疲劳对注意能力影响的 ERP 研究. 西安: 第四军医大学, 2011.
5. 周佳. 人体脑力疲劳状态识别及改善方法研究. 秦皇岛: 燕山大学, 2012.
6. 刘晓明, 李俐莹, 蔡巍, 等. 电场场域可视性复杂网络模型研究. 电工技术学报, 2016, 31(3): 89-96.
7. 温洪, 郭浩, 李越, 等. 重度抑郁症患者脑功能网络的分类研究. 计算机应用研究, 2013, 30(8): 2304-2307.
8. 梁红, 相洁. 基于 MCI 患者脑功能网络的分类研究. 计算机工程与设计, 2014, 35(4): 1390-1394.
9. 尹宁. 基于脑电的磁刺激穴位复杂脑功能脑网络研究. 天津: 河北工业大学, 2013.
10. Achard S, Bullmore E. Efficiency and cost of economical brain functional networks. PLoS Comput Biol, 2007, 3(2): e17.
11. 高佳, 王蔚. 基于稀疏贝叶斯网络的情绪脑电的有效性脑网络研究. 生物医学工程学杂志, 2015, 32(5): 945-951.
12. 郭磊, 王瑶, 于洪丽, 等. 基于近似熵的磁刺激穴位脑功能网络构建与分析. 电工技术学报, 2015, 30(10): 31-38.
13. 尤佳. 基于 fMRI 的不同状态下脑功能网络的研究. 天津: 河北工业大学, 2015.
14. Vapnik V N. The nature of statistical learning theory. New York: Springer-Verlag, 1995.
15. 李钢, 王蔚, 张胜. 支持向量机在脑电信号分类中的应用. 计算机应用, 2006, 26(6): 1431-1433, 1436.
16. 徐晓明. SVM 参数寻优及其在分类中的应用. 大连: 大连海事大学, 2014.
17. Wong T T. Parametric methods for comparing the performance of two classification algorithms evaluated by k-fold cross validation on multiple data sets. Pattern Recognit, 2017, 65: 97-107.
18. 王兴玲, 李占斌. 基于网格搜索的支持向量机核参数的确定. 中国海洋大学学报, 2005, 35(5): 859-862.
19. Duarte E, Wainer J. Empirical comparison of cross-validation and internal metrics for tuning SVM hyperparameters. Pattern Recognit Lett, 2017, 88(1): 6-11.

Journal of Biomedical Engineering

Research on classification of brain functional network features during mental fatigue

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