<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">CDCST</journal-id><journal-title-group><journal-title>China Daily Chemical Science Technology</journal-title></journal-title-group><issn>2997-7096</issn><eissn>2997-710X</eissn><publisher><publisher-name>Art and Technology</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/CDCST.8001</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>探索情绪护肤：神经美容产品的多元化测量技术</title><url>https://artdesignp.com/journal/CDCST/1/2/10.61369/CDCST.8001</url><author>吴梦洁,黄智健,高蕾,姚纯萍,林文强</author><pub-date pub-type="publication-year"><year>2024</year></pub-date><volume>1</volume><issue>2</issue><history><date date-type="pub"><published-time>2024-11-25</published-time></date></history><abstract>情绪护肤领域专注于开发能够提升情绪和幸福感的化妆品和个人护理产品。神经美容作为其科学基础，探讨了这些产品如何通过影响神经系统来产生效果。文章综述了评估情绪改善效果的三种主要测量方法：心理学测量、生理学测量和外显性行为测量。心理学测量通过心理学量表来评估个体的心理特征、态度、情绪和行为等方面的数据。生理学测量通过监测脑电、皮肤温度、心率、皮电活动等生理参数，提供了情绪变化的客观指标。外显性行为测量则关注情绪的外在表现，通过面部表情识别和眼动追踪技术来评估情绪反应。尽管这些方法在情绪护肤领域的应用前景广阔，但它们在实际应用中仍面临技术挑战和操作限制。未来的研究需要解决这些挑战，发展更高效、便捷的情绪监测技术，以促进情绪护肤产品的科学评价和市场发展。</abstract><keywords>情绪护肤 ,神经美容 ,脑电图 ,面部表情识别 ,生理测量</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1] Plutchik R. A psychoevolutionary theory of emotions [Z]. Sage Publications. 1982.&amp;nbsp;[2] 王亚男，梁妍琳，李健．心理测量技术在科研型事业单位人力 资源中的应用 [J]. 中国计量，2020, (11): 14-6.&amp;nbsp;[3] 马楠．基于SAM情绪量表的食品包装视觉要素研究 [J]. 现代食 品，2022, 28(19): 93-5.&amp;nbsp;[4] 吉楠，李幼穗．《大学生主观幸福感量表》的编制 [J]. 心理与 行为研究，2006, (01): 49-54.&amp;nbsp;[5] Dadebayev D, Goh W W, Tan E X. EEG-based emotion recognition: Review of commercial EEG devices and machine learning techniques [J]. Journal of King Saud University-Computer and Information Sciences, 2022, 34(7): 4385-401.&amp;nbsp;[6] Wang X, Ren Y, Luo Z, et al. Deep learning-based EEG emotion recognition: Current trends and future perspectives [J]. Frontiers in psychology, 2023, 14: 1126994.&amp;nbsp;[7] Roso A, Aubert A, Cambos S, et al. Contribution of cosmetic ingredients and skin care textures to emotions [J]. International Journal of Cosmetic Science, 2024, 46(2): 262-83.&amp;nbsp;[8] Moriya H, Machida A, Munakata T, et al. Relationships between subjective experience, electroencephalogram, and heart rate variability during a series of cosmetic behavior [J]. Frontiers in Psychology, 2024, 15: 1225737.&amp;nbsp;[9] Al-Nafjan A, Hosny M, Al-Wabil A, et al. Classification of Human Emotions from Electroencephalogram (EEG) Signal using Deep Neural Network [J]. International Journal of Advanced Computer Science and Applications, 2017, 8(9).&amp;nbsp;[10] Springer A, H&amp;ouml;ckmeier L, Schicker D, et al. Measurement of stress relief during scented cosmetic product application using a mood questionnaire, stress hormone levels and brain activation [J]. Cosmetics, 2022, 9(5): 97.&amp;nbsp;[11] Kokubo H, Kawano K. EEG Measurements on Relaxation Caused by Essence of Colloidal Platinum for Skin Care A Discussion Using dB Analysis [J]. Journal of International Society of Life Information Science, 2016, 34(2): 109-19.&amp;nbsp;[12] Bouhout S, Aubert A, Vial F, et al. Physiological benefits associated with facial skincare: Well ‐being from emotional perception to neuromodulation [J]. International Journal of Cosmetic Science, 2023, 45(4): 458-69.&amp;nbsp;[13] Park K H, Kim H J, Oh B, et al. Evaluation of human electroencephalogram change for sensory effects of fragrance [J]. Skin Research and Technology, 2019, 25(4): 526-31.&amp;nbsp;[14] Alezzi A, Kamel N, Faye I, et al. EEG Frontal Theta-Beta Ratio and Frontal Midline Theta for the Assessment of Social Anxiety Disorder [C]. Proceedings of the 2020 10th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), IEEE, 2020: 107-112.&amp;nbsp;[15] 周正，吴梦洁，王谊，等．敷面膜对消费者情绪放松的脑电 波研究 [J]. 日用化学工业，2021, 51(7): 8.&amp;nbsp;[16] Aldayel M, Ykhlef M, Al-Nafjan A. Deep learning for EEG-based preference classification in neuromarketing [J]. Applied Sciences, 2020, 10(4): 1525.&amp;nbsp;[17] Al-Nafjan A, Hosny M, Al-Wabil A, et al. Classification of human emotions from electroencephalogram (EEG) signal using deep neural network [J]. Int J Adv Comput Sci Appl, 2017, 8(9): 419-25.&amp;nbsp;[18] Gabriel D, Merat E, Jeudy A, et al. Emotional effects induced by the application of a cosmetic product: a real-time electrophysiological evaluation [J]. Applied Sciences, 2021, 11(11): 4766.&amp;nbsp;[19] 张冠华，余旻婧，陈果，等．面向情绪识别的脑电特征研究 综述 [J]. 中国科学：信息科学，2019, 49(09): 1097-118.&amp;nbsp;[20] Ding R, Li P, Wang W, et al. Emotion processing by ERP combined with development and plasticity [J]. Neural plasticity, 2017, 2017(1): 5282670.&amp;nbsp;[21] Utama N P, Takemoto A, Koike Y, et al. *Phased processing of facial emotion: an ERP study [J]. Neuroscience Research, 2009, 64(1): 30-40.&amp;nbsp;[22] Kaneki N, Kurosaka T, Yamada H, et al. Effect of aroma on event-related potential in cogivitive workload [J]. Kansei Engineering International, 2005, 5: 51-6.&amp;nbsp;[23] Conroy M A, Polich J. Affective valence and P300 when stimulus arousal level is controlled [J]. Cognition and emotion, 2007, 21(4): 891-901.&amp;nbsp;[24] Recio G, Schacht A, Sommer W. Recognizing dynamic facial expressions of emotion: Specificity and intensity effects in eventrelated brain potentials [J]. Biological psychology, 2014, 96: 111-25.&amp;nbsp;[25] 陈旭东，钟恒，皇甫洁，等．脑电信号情绪识别综述 [J]. 计算 机应用，2023, 43(S1): 323-32.&amp;nbsp;[26] Kim J, Hwang D-U, Son E J, et al. Emotion recognition while applying cosmetic cream using deep learning from EEG data; crosssubject analysis [J]. Plos one, 2022, 17(11): e0274203.&amp;nbsp;[27] Schmidt P, Reiss A, Duerichen R, et al. Wearable affect and stress recognition: A review [J]. arXiv preprint arXiv:181108854, 2018.&amp;nbsp;[28] Kim K H, Bang S W, Kim S R. Emotion recognition system using short-term monitoring of physiological signals [J]. Medical and biological engineering and computing, 2004, 42: 419-27.&amp;nbsp;[29] Shoumy N J, Ang L-M, Seng K P, et al. Multimodal big data affective analytics: A comprehensive survey using text, audio, visual and physiological signals [J]. Journal of Network and Computer Applications, 2020, 149: 102447.&amp;nbsp;[30] Kreibig S D. Autonomic nervous system activity in emotion: A review [J]. Biological psychology, 2010, 84(3): 394-421.&amp;nbsp;[31] Bota P J, Wang C, Fred A L, et al. A review, current challenges, and future possibilities on emotion recognition using machine learning and physiological signals [J]. IEEE access, 2019, 7: 140990-1020.&amp;nbsp;[32] Freiherr J, Schicker D, Springer A. How to Successfully Prove Psychophysiological Effects in Cosmetic Products [J]. SOFW Journal (English version), 2023, 149: 42-44.&amp;nbsp;[33] 郎学美，高静威，李欣励，等．情绪知觉的新视角：面部表 情与躯体表情的整合 [J]. 心理研究，2024, 17(4): 291-300.&amp;nbsp;[34] 盛丹怡，卢奇，程时伟．基于眼动跟踪的情绪识别方法研究 [J]. 人类工效学，2022, 28(06): 57-62.&amp;nbsp;[35] Mehrabian A. Communication without words [M]. Communication theory. Routledge. 2017: 193-200.&amp;nbsp;[36] 杨傲林，张哲婷，姜可欣，等．基于表情符号的消费者测评 结合GC-MS对冷萃咖啡与热萃咖啡风味特征的研究 [J]. 食品科学 技术学报，2024: 1-29.&amp;nbsp;[37] 黄翠，武运，薛洁，等．基于面部表情分析技术的葡萄酒中 关键香气与饮用舒适度相关性评价 [J]. 食品与发酵工业，2024, 50(08): 146-57.&amp;nbsp;[38] Friesen E, Ekman P. Facial action coding system: a technique for the measurement of facial movement [J]. Palo Alto, 1978, 3(2): 5.&amp;nbsp;[39] 徐小康．基于深度学习的表情识别研究与应用 [D]. 上海： 东 华大学，2022.&amp;nbsp;[40] Mohammadi M R, Fatemizadeh E, Mahoor M H. PCA-based dictionary building for accurate facial expression recognition via sparse representation [J]. Journal of Visual Communication and Image Representation, 2014, 25(5): 1082-92.&amp;nbsp;[41] Liu Z, Li S, Cao W, et al. Combining 2D gabor and local binary pattern for facial expression recognition using extreme learning machine [J]. Journal of Advanced Computational Intelligence and Intelligent Informatics, 2019, 23(3): 444-55.&amp;nbsp;[42] 石建军，许键．眼动跟踪技术研究进展 [J]. 光学仪器，2019, 41(03): 87-94.&amp;nbsp;[43] Lim J Z, Mountstephens J, Teo J. Emotion recognition using eyetracking: taxonomy, review and current challenges [J]. Sensors, 2020, 20(8): 2384.&amp;nbsp;[44] 刘帝欣．视觉表象生成中的眼动指标评述 [J]. 心理学进展， 2021, 11(10): 7.&amp;nbsp;[45] Larsson L, Nystr&amp;ouml;m M, Andersson R, et al. Detection of fixations and smooth pursuit movements in high-speed eye-tracking data [J]. Biomedical Signal Processing and Control, 2015, 18: 145-52.&amp;nbsp;[46] 闫国利，熊建萍，臧传丽，等．阅读研究中的主要眼动指标 评述 [J]. 心理科学进展，2013, 21(04): 589-605.&amp;nbsp;[47] Wang F, Ma X, Cheng D, et al. Electroencephalography as an objective method for assessing subjective emotions during the application of cream [J]. Skin Research and Technology, 2024, 30(4): e13692.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
