生物医学工程学杂志

生物医学工程学杂志

人类交互行为的隐马尔可夫模型

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为了满足临床中单个患者合作与竞争实验的需求,本文提出了两个基于隐马尔可夫模型的人类交互行为按键模型。基于两个按键模型,设计了验证实验并采集了有效被试行为学数据和前额叶脑血氧数据用于模型的评价。利用被试参与度与模型预测得分之间的相关性评价模型合理性;利用被试按键时间信息评价模型行为学模拟的准确率;提取前额深层信息,从信号同步性关系评价模型对生理学信息的提取。合理性评价表明合作按键模型在训练数据和测试数据的相关系数别为 0.883 1 和 0.578 6,竞争模型则分别为 0.813 1 和 0.617 8。行为学信息评价结果表明,两种模型对被试行为学模拟的准确率都达到 71.43%。生理学评价结果表明,合作按键模型与竞争按键模型能够提取到被试的前额叶深层信息,且该信息与双人合作按键与竞争按键提取到的信息具有一致性。综上所述,合作、竞争两种按键模型的行为学、生理学评价结果显示,模型的实际表现与人-人的交互过程一致性较高,因此可考虑用于临床试验研究。

In order to meet the requirements in the cooperation and competition experiments for an individual patient in clinical application, two human interactive behavior key-press models based on hidden Markov model (HMM) were proposed. To validate the cooperative and competitive models, a verification experimental task was designed and the data were collected. The correlation of the score and subjects’ participation level has been used to analyze the reasonability verification. Behavior verification was conducted by comparing the statistical difference in response time for subjects between human-human and human-computer experiment. In order to verify the physiological validity of the models, we have utilized the coherence analysis to analyze the deep information of prefrontal brain area. Reasonability verification shows that the correlation coefficient for the training data and the testing data is 0.883 1 and 0.578 6 respectively based on cooperation model, and 0.813 1 and 0.617 8 respectively based on the competition model. The behavioral verification result shows that the cooperation and competition models have an accuracy of 71.43% respectively. The results of physiological validity show that the deep information of prefrontal brain area could been extracted based on the cooperation and competition models, and reveal the consistency of coherence between the double key-press cooperative and competitive experiments, respectively. Above all, the high consistency is obtained between the cooperatio/competition model and the double key-press experiment by the behavioral and physiological evaluation results. Consequently, the cooperation and competition models could be applied to clinical trials.

关键词: 隐马尔可夫模型; 人类交互行为; 合作; 竞争; 多变量经验模态分解

Key words: hidden Markov model; human interactive behavior; cooperation; competition; multivariate empirical mode decomposition

引用本文: 覃龙, 高琳, 张权, 王逸飞, 魏玉会, 闫相国. 人类交互行为的隐马尔可夫模型. 生物医学工程学杂志, 2019, 36(1): 40-49. doi: 10.7507/1001-5515.201705037 复制

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1. Piggot J, Kwon H, Mobbs D, et al. Emotional attribution in high-functioning individuals with autistic spectrum disorder: a functional imaging study. J Am Acad Child Adolesc Psychiatry, 2004, 43(4): 473-480.
2. Watson C, Hoeft F, Garrett A S, et al. Aberrant brain activation during gaze processing in boys with fragile X syndrome. Arch Gen Psychiatry, 2008, 65(11): 1315-1323.
3. Reiss A L, Eckert M A, Rose F E, et al. An experiment of nature: brain anatomy parallels cognition and behavior in Williams syndrome. J Neurosci, 2004, 24(21): 5009-5015.
4. Cui Xu, Bryant D M, Reiss A L. NIRS-based hyperscanning reveals increased interpersonal coherence in superior frontal cortex during cooperation. Neuroimage, 2012, 59(3): 2430-2437.
5. 覃龙, 王严锋, 闫相国. 利用基于近红外光谱的超扫描技术研究人类交互行为. 国际神经精神科学杂志, 2015, 4(4): 19-27.
6. Rabiner L R. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 1989, 77(2): 257-286.
7. 张彩虹. 隐马氏模型的建模及其应用. 长沙: 国防科学技术大学, 2004.
8. Gales M, Young S. The application of hidden Markov models in speech recognition. Foundations & Trends in Signal Processing, 2008, 1(1/2): 195-304.
9. Bicego M, Castellani U, Murino V. Using hidden Markov models and wavelets face recognition//Proceedings of IEEE International Conference on Image Analysis and Processing (ICIAP03). Italy: IEEE Computer Society, 2003: 52-56.
10. Qian K, Ma X D, Dai X Z. Motion activity recognition based on abstract hidden Markov model. Pattern Recognition & Artificial Intelligence, 2009, 22(3): 433-439.
11. Vlontzos J A, Kung S Y. Hidden Markov models for character recognition. IEEE Trans Image Process, 1992, 1(4): 539-543.
12. 陶新民, 徐晶, 杜宝祥, 等. 基于小波域隐马尔可夫模型故障诊断方法. 振动与冲击, 2009, 28(4): 33-37.
13. Zhang Quan, Brown E N, Strangman G E. Adaptive filtering for global interference cancellation and real-time recovery of evoked brain activity: A Monte Carlo simulation study. J Biomed Opt, 2007, 12(4): 044014-044026.
14. Rehman N, Mandic D P. Multivariate empirical mode decomposition. Proceedings of the Royal Society A, 2010, 466(2117): 1291-1302.
15. Torrence C, Webster P J. Interdecadal changes in the ENSO-monsoon system. J Clim, 1999, 12(8): 2679-2690.
16. Konvalinka I, Xygalatas D, Bulbulia J, et al. Synchronized arousal between performers and related spectators in a fire-walking ritual. Proc Natl Acad Sci U S A, 2011, 108(20): 8514-8519.
17. Muller V, Lindenberger U. Cardiac and respiratory patterns synchronize between persons during choir singing. PLoS One, 2011, 6(9): e24893.