光学学报, 2016, 36 (3): 0317002, 网络出版: 2016-03-03   

基于功能性近红外光谱技术识别情绪状态 下载: 679次

Emotional State Recognition Based on Functional Near-Infrared Spectroscopy
作者单位
中国航天员科研训练中心人因工程重点实验室, 北京 100091
摘要
利用功能性近红外光谱(fNIRs)技术实现了对不同情绪状态的识别。采集了15名受试者在6种情绪种类图片刺激下的fNIRs信号以及唤醒度、愉悦度评价数据。为了实现对受试者情绪状态的分类评估,采用支持向量机(SVM)和基于支持向量机的递归特征筛选(SVM-RFE)算法来筛选参数并设计情绪状态的分类器。结果表明在多种情绪种类图片刺激下,受试者出现了显著的功能响应曲线,并且在唤醒度、愉悦度和情绪种类三个分类目标上分别实现了81%、78.78%和68%的平均分类正确率。同时发现唤醒度和愉悦度的敏感特征主要出现在眶额叶皮层和背外侧皮层,且近似熵是反映情绪状态变化的有效指标。因此采用fNIRs能够基本实现对人体情绪状态的识别。
Abstract
In order to investigate the human emotional state recognition, the functional near-infrared spectroscopy (fNIRs) technique is applied to measure hemodynamic signals of 15 participants who are requested to see six types of pictures, and the participants have to complete 7-point rating scale of valence and arousal after every picture stimulus. The support vector machine (SVM) and support vector machine based recursive feature elimination (SVMRFE) algorithm are applied to design classifiers. Under different emotional image stimulus, the hemodynamic signals of some participants show significant neural response. With the target classification based on valence, arousal and emotion category, the accuracy is 81%, 78.78% and 68%, respectively. The 5th and 6th channels for fNIRs measurement are significantly sensitive to arousal and valence state, and the two channels are located at orbitonfrontal cortex and dorsolateral prefrontal cortex regions. Besides, it is found that the entropy of fNIRs can reflect the variation in emotional state effectively. The results suggest that fNIRs can be used for recognition of human emotional state.功能状态等方面的研究。
参考文献

[1] Tuscan L A, Herbert J D, Forman E M, et al.. Exploring frontal asymmetry using functional near-infrared spectroscopy: A preliminary study of the effects of social anxiety during interaction and performance tasks[J]. Brain Imaging & Behavior, 2013, 7(2): 140-153.

[2] Boas D A, Elwell C E, Ferrari M, et al.. Twenty years of functional near-infrared spectroscopy: Introduction for the special issue[J]. NeuroImage, 2014, 85(2): 1-5.

[3] Ferrari M, Quaresima V. A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application[J]. NeuroImage, 2012, 63(2): 921-935.

[4] Doi H, Nishitani S, Shinohara K. NIRS as a tool for assaying emotional function in the prefrontal cortex[J]. Frontiers in Human Neuroscience, 2013, 7(1): 56-67.

[5] Davidson R J, Putnam K M, Larson C L. Dysfunction in the neural circuitry of emotion regulation — a possible prelude to violence[J]. Science, 2000, 289(5479): 591-594.

[6] Balconi M, Lucchiari C. Consciousness and arousal effects on emotional face processing as revealed by brain oscillations. A gamma band analysis[J]. International Journal of Psychophysiology, 2008, 67(1): 41-46.

[7] Balconi M, Grippa E, Vanutelli M E. What hemodynamic (fNIRS), electrophysiological (EEG) and autonomic integrated measures can tell us about emotional processing[J]. Brain & Cognition, 2015, 95: 67-76.

[8] 熊洋, 司民真, 高飞, 等. 基于NIR-SERS光谱技术分析宫颈癌氧合血红蛋白[J]. 中国激光, 2015, 42(1): 0115001.

    Xiong Yang, Si Minzhen, Gao Fei, et al.. Study on cervical cancer oxyhemoglobin using near-infrared surface-enhanced Raman spectroscopy[J]. Chinese J Lasers, 2015, 42(1): 0115001.

[9] 张永, 陈斌, 李东. 一种模拟生物组织内光传播的三维几何蒙特卡洛方法[J]. 中国激光, 2015, 42(1): 0104003.

    Zhang Yong, Chen Bin, Li Dong. A three-dimensional geometric Monte Carlo method for simulation of light propagation in biological tissues[J]. Chinese J Lasers, 2015, 42(1): 0104003.

[10] 吴春阳, 卢启鹏, 丁海泉, 等. 利用人体组织液进行近红外无创血糖测量[J]. 光学学报, 2013, 33(11): 1117001.

    Wu Chunyang, Lu Qipeng, Ding Haiquan, et al.. Noninvasive blood glucose sensing with near-infrared spectroscopy based on interstitial fluid[J]. Acta Optica Sinica, 2013, 33(11): 1117001.

[11] Hoshi Y, Huang J, Kohri S, et al.. Recognition of human emotions from cerebral blood flow changes in the frontal region: A study with event-related near-infrared spectroscopy[J]. Journal of Neuroimaging, 2009, 21(2): 94-101.

[12] 潘津津, 焦学军, 焦典, 等. 利用功能性近红外光谱法研究大脑皮层血氧情况随任务特征变化规律[J]. 光学学报, 2015, 35(8): 0817001.

    Pan Jinjin, Jiao Xuejun, Jiao Dian, et al.. Study on variation in cortex oxygen with task features using functional near-infrared spectroscopy [J]. Acta Optica Sinica, 2015, 35(8): 0817001.

[13] Dieler A C, Plichta M M, Sler T, et al.. Suppression of emotional words in the Think/No-Think paradigm investigated with functional near-infrared spectroscopy[J]. International Journal of Psychophysiology, 2010, 78(2): 129-135.

[14] Sourina O, Wang Q, Liu Y, et al.. A real-time fractal-based brain state recognition from EEG and its applications[C]. Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing, Rome, 2011: 82-90.

[15] Lischke A, Berger C, Prehn K, et al.. Intranasal oxytocin enhances emotion recognition from dynamic facial expressions and leaves eyegaze unaffected[J]. Psychoneuroendocrinology, 2012, 37(4): 475-481.

[16] Khalili Z, Moradi M H. Emotion recognition system using brain and peripheral signals: Using correlation dimension to improve the results of EEG[C]. IEEE International Joint Conference on Neural Networks, 2009: 1571-1575.

[17] Hoshi Y, Huang J, Kohri S, et al.. Recognition of human emotions from cerebral blood flow changes in the frontal region: A study with event-related near-infrared spectroscopy[J]. Journal of Neuroimaging, 2009, 21(2): 94-101.

[18] Liu Y, Sourina O, Nguyen M K. Real-time EEG-based emotion recognition and its applications[M]. //Transactions on Computational Science XII, 2011, 6670: 256-277.

[19] Lang P J, Bradley M M, Cuthbert B N. International affective picture system (IAPS): Affective ratings of pictures and instruction manual [R]. University of Florida, 2008, Technical Report A-8.

[20] Bradley M M, Lang P J. Measuring emotion: The self-assessment manikin and the semantic differential[J]. Journal of Behavior Therapy & Experimental Psychiatry, 1994, 25(1): 49-59.

[21] Hu T Y, Xie X, Li J. Negative or positive The effect of emotion and mood on risky driving[J]. Transportation Research Part F: Traffic Psychology & Behaviour, 2013, 16: 29-40.

[22] Jeon M, Walker B N, Yim J B. Effects of specific emotions on subjective judgment, driving performance, and perceived workload[J]. Transportation Research Part F: Traffic Psychology & Behaviour, 2014, 24: 197-209.

[23] Kirilina E, Jelzow A, Heine A, et al.. The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy [J]. NeuroImage, 2012, 61(1): 70-81.

[24] 潘津津, 焦学军, 姜劲, 等. 利用功能性近红外光谱成像方法评估脑力负荷[J]. 光学学报, 2014, 34(11): 1130002.

    Pan Jinjin, Jiao Xuejun, Jiang Jin, et al.. Mental workload assessment based on functional near-infrared spectroscopy[J]. Acta Optica Sinica, 2014, 34(11): 1130002.

[25] Haeussinger F B, Sler T, Heinzel S, et al.. Reconstructing functional near-infrared spectroscopy (fNIRS) signals impaired by extra-cranial confounds: An easy-to-use filter method[J]. NeuroImage, 2014, 95(8): 69-79.

[26] Cui X, Bray S, Reiss A L. Functional near infrared spectroscopy (NIRS) signal improvement based on negative correlation between oxygenated and deoxygenated hemoglobin dynamics[J]. NeuroImage, 2010, 49(4): 3039-3046.

[27] 周振宇, 杨宏宇, 龚辉, 等. 基于希尔伯特黄变换的近红外脑功能成像信号分析[J]. 光学学报, 2007, 27(2): 307-312.

    Zhou Zhenyu, Yang Hongyu, Gong Hui, et al.. Brain signal analysis of functional near-infrared imaging based on Hilbert-Huang transform [J]. Acta Optica Sinica, 2007, 27(2): 307-312.

[28] 彭明金, 李智. 基于希尔伯特-黄变换的激光微多普勒信号分析与特征提取[J]. 中国激光, 2013, 40(8): 0809004.

    Peng Mingjin, Li Zhi. Analysis and feature extraction of laser micro-Doppler signatures based on Hilbert-Huang transforms[J]. Chinese J Lasers, 2013, 40(8): 0809004.

[29] 许露. 基于SVM-RFE 和粒子群算法的特征选择算法研究[D]. 长沙:湖南师范大学, 2014: 63-75.

    Xu Lu. A Study on Feature Selection Algorithm based on SVM-RFE and Particle Swarm Optimization[D]. Changsha: Hunan Normal University, 2014: 63-75.

[30] Naseer N, Hong K S. Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain-computer interface[J]. Neuroscience Letters, 2013, 553(8): 84-89.

[31] Kaiser V, Bauernfeind G, Kreilinger A, et al.. Cortical effects of user training in a motor imagery based brain-computer interface measured by fNIRS and EEG[J]. NeuroImage, 2014, 85(1): 432-444.

姜劲, 焦学军, 潘津津, 张朕, 曹勇, 肖毅. 基于功能性近红外光谱技术识别情绪状态[J]. 光学学报, 2016, 36(3): 0317002. Jiang Jin, Jiao Xuejun, Pan Jinjin, Zhang Zhen, Cao Yong, Xiao Yi. Emotional State Recognition Based on Functional Near-Infrared Spectroscopy[J]. Acta Optica Sinica, 2016, 36(3): 0317002.

本文已被 2 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!