{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T20:08:41Z","timestamp":1775074121024,"version":"3.50.1"},"reference-count":69,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,4,13]],"date-time":"2020-04-13T00:00:00Z","timestamp":1586736000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>We analyzed the contribution of electroencephalogram (EEG) data, age, sex, and personality traits to emotion recognition processes\u2014through the classification of arousal, valence, and discrete emotions labels\u2014using feature selection techniques and machine learning classifiers. EEG traits and age, sex, and personality traits were retrieved from a well-known dataset\u2014AMIGOS\u2014and two sets of traits were built to analyze the classification performance. We found that age, sex, and personality traits were not significantly associated with the classification of arousal, valence and discrete emotions using machine learning. The added EEG features increased the classification accuracies (compared with the original report), for arousal and valence labels. Classification of arousal and valence labels achieved higher than chance levels; however, they did not exceed 70% accuracy in the different tested scenarios. For discrete emotions, the mean accuracies and the mean area under the curve scores were higher than chance; however, F1 scores were low, implying that several false positives and false negatives were present. This study highlights the performance of EEG traits, age, sex, and personality traits using emotion classifiers. These findings could help to understand the traits relationship in a technological and data level for personalized human-computer interactions systems.<\/jats:p>","DOI":"10.3390\/make2020007","type":"journal-article","created":{"date-parts":[[2020,4,14]],"date-time":"2020-04-14T03:10:01Z","timestamp":1586833801000},"page":"99-124","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Analysis of Personality and EEG Features in Emotion Recognition Using Machine Learning Techniques to Classify Arousal and Valence Labels"],"prefix":"10.3390","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2373-6312","authenticated-orcid":false,"given":"Laura Alejandra","family":"Mart\u00ednez-Tejada","sequence":"first","affiliation":[{"name":"FIRST Institute of Innovative Research, Tokyo Institute of Technology, Yokohama 226-0026, Japan"}]},{"given":"Yasuhisa","family":"Maruyama","sequence":"additional","affiliation":[{"name":"FIRST Institute of Innovative Research, Tokyo Institute of Technology, Yokohama 226-0026, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7495-9113","authenticated-orcid":false,"given":"Natsue","family":"Yoshimura","sequence":"additional","affiliation":[{"name":"FIRST Institute of Innovative Research, Tokyo Institute of Technology, Yokohama 226-0026, Japan"}]},{"given":"Yasuharu","family":"Koike","sequence":"additional","affiliation":[{"name":"FIRST Institute of Innovative Research, Tokyo Institute of Technology, Yokohama 226-0026, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jeon, M. (2017). Chapter 1 - Emotions and Affect in Human Factors and Human-Computer Interaction: Taxonomy, Theories, Approaches, and Methods. Emotions and Affect in Human Factors and Human-Computer Interaction, Elsevier.","DOI":"10.1016\/B978-0-12-801851-4.00001-X"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Rukavina, S., Gruss, S., Hoffmann, H., Tan, J.-W., Walter, S., and Traue, H.C. (2016). Affective computing and the impact of gender and age. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0150584"},{"key":"ref_3","first-page":"1","article-title":"The neurobiology of emotion-cognition interactions: Fundamental questions and strategies for future research","volume":"9","author":"Hendler","year":"2015","journal-title":"Front. Hum. Neurosci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Laborde, S. (2016). Bridging the Gap between Emotion and Cognition: An Overview. Perform. Psychol. Percept. Act. Cognit. Emot., 275\u2013289.","DOI":"10.1016\/B978-0-12-803377-7.00017-X"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1037\/a0024244","article-title":"Discrete emotions predict changes in cognition, judgment, experience, behavior, and physiology: A meta-analysis of experimental emotion elicitations","volume":"137","author":"Lench","year":"2011","journal-title":"Psychol. Bull."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1109\/TAFFC.2014.2330816","article-title":"A Survey of Personality Computing","volume":"5","author":"Vinciarelli","year":"2014","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/0747-5632(91)90002-I","article-title":"Personality factors in human-computer interaction: A review of the literature","volume":"7","author":"Pocius","year":"1991","journal-title":"Comput. Human Behav."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1007\/BF02344719","article-title":"Emotion recognition system using short-term monitoring of physiological signals","volume":"42","author":"Kim","year":"2004","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1037\/h0077714","article-title":"A circumplex model of affect","volume":"39","author":"Russell","year":"1980","journal-title":"J. Pers. Soc. Psychol."},{"key":"ref_10","first-page":"981","article-title":"Affective computing: A review","volume":"3784","author":"Tao","year":"2005","journal-title":"Affect. Comput. Intell. Interact."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Al-Nafjan, A., Hosny, M., Al-Ohali, Y., and Al-Wabil, A. (2017). Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review. Appl. Sci., 7.","DOI":"10.3390\/app7121239"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"10840","DOI":"10.1109\/ACCESS.2018.2809453","article-title":"Brain-Computer Interface Control in a Virtual Reality Environment and Applications for the Internet of Things","volume":"6","author":"Coogan","year":"2018","journal-title":"IEEE Access"},{"key":"ref_13","first-page":"1","article-title":"A Review on the Computational Methods for Emotional State Estimation from the Human EEG","volume":"2013","author":"Kim","year":"2013","journal-title":"Comput. Math. Methods Med."},{"key":"ref_14","first-page":"1","article-title":"Emotions Recognition Using EEG Signals: A Survey","volume":"3045","author":"Alarcao","year":"2017","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Liu, Y., Sourina, O., and Nguyen, M.K. (2010, January 20\u201322). Real-time EEG-based human emotion recognition and visualization. Proceedings of the 2010 International Conference on Cyberworlds, Singapore.","DOI":"10.1109\/CW.2010.37"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Jatupaiboon, N., Pan-Ngum, S., and Israsena, P. (2013, January 29\u201331). Emotion classification using minimal EEG channels and frequency bands. Proceedings of the 2013 10th International Joint Conference on Computer Science and Software Engineering, Maha Sarakham, Thailand.","DOI":"10.1109\/JCSSE.2013.6567313"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.ijpsycho.2009.08.006","article-title":"Brain oscillations and BIS\/BAS (behavioral inhibition\/activation system) effects on processing masked emotional cues. ERS\/ERD and coherence measures of alpha band","volume":"74","author":"Balconi","year":"2009","journal-title":"Int. J. Psychophysiol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1007\/s11517-011-0747-x","article-title":"Spectral EEG frontal asymmetries correlate with the experienced pleasantness of TV commercial advertisements","volume":"49","author":"Vecchiato","year":"2011","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/0278-2626(92)90065-T","article-title":"Anterior cerebral asymmetry and the nature of emotion","volume":"20","author":"Davidson","year":"1992","journal-title":"Brain Cogn."},{"key":"ref_20","unstructured":"Li, M., and Lu, B.L. (2009, January 3\u20136). Emotion classification based on gamma-band EEG. Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, Minneapolis, MN, USA."},{"key":"ref_21","first-page":"201","article-title":"Emotion recognition based on the asymmetric left and right activation","volume":"3","author":"Park","year":"2011","journal-title":"Int. J. Med. Med. Sci."},{"key":"ref_22","unstructured":"Kandel, E.R., Schwartz, J.H., and Jessell, T.M. (2013). Principles of Neural Science, McGraw-Hill. [5th ed.]."},{"key":"ref_23","unstructured":"American Psychological Association (2020, April 13). \u201cPersonality,\u201d APA. Available online: https:\/\/www.apa.org\/topics\/personality\/."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Eysenck, H.J., and Eysenck, S.B.G. (1994). Manual of the Eysenck Personality Questionnaire: (EPQ-R Adult), EdITS\/Educational and Industrial Testing Service.","DOI":"10.1037\/t05461-000"},{"key":"ref_25","unstructured":"McCrae, R.R., and Costa, P.T. (1999). A Five-Factor theory of personality. Handbook of Personality: Theory and Research, Guilford Press. [2nd ed.]."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Gray, J.A. (1981). A Critique of Eysenck\u2019s Theory of Personality. A Model for Personality, Springer.","DOI":"10.1007\/978-3-642-67783-0_8"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1037\/0022-3514.86.2.356","article-title":"A Six-Factor Structure of Personality-Descriptive Adjectives: Solutions from Psycholexical Studies in Seven Languages","volume":"86","author":"Ashton","year":"2004","journal-title":"J. Pers. Soc. Psychol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, H., Pang, N., Guo, S., and Wang, H. (2007, January 15\u201318). Research on textual emotion recognition incorporating personality factor. Proceedings of the 2007 IEEE International Conference on Robotics and Biomimetics (ROBIO), Sanya, China.","DOI":"10.1109\/ROBIO.2007.4522515"},{"key":"ref_29","first-page":"46","article-title":"Annotation-Based Learner\u2019S Personality Modeling in Distance Learning Context","volume":"17","author":"Omheni","year":"2016","journal-title":"Turkish Online J. Distance Educ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wei, W.L., Wu, C.H., Lin, J.C., and Li, H. (2013, January 26\u201331). Interaction style detection based on Fused Cross-Correlation Model in spoken conversation. Proceedings of the ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6639323"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Fallahnezhad, M., Vali, M., and Khalili, M. (2017, January 2\u20134). Automatic Personality Recognition from reading text speech. Proceedings of the 2017 Iranian Conference on Electrical Engineering (ICEE), Tehran, Iran.","DOI":"10.1109\/IranianCEE.2017.7985447"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1109\/TMM.2016.2522763","article-title":"Multimodal Personality Recognition in Collaborative Goal-Oriented Tasks","volume":"18","author":"Batrinca","year":"2016","journal-title":"IEEE Trans. Multimed."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Alam, F., and Riccardi, G. (2014, January 7). Predicting personality traits using multimodal information. Proceedings of the 2014 Workshop on Computational Personality Recognition, Workshop of MM 2014, WCPR 2014, Orlando, FL, USA.","DOI":"10.1145\/2659522.2659531"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Batrinca, L., Lepri, B., and Pianesi, F. (2011, January 1). Multimodal recognition of personality during short self-presentations. Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops - JHGBU 2011 Workshop, J-HGBU\u201911, MM\u201911, Scottsdale, AZ, USA.","DOI":"10.1145\/2072572.2072583"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Guo, A., and Ma, J. (2018). Archetype-based modeling of persona for comprehensive personality computing from personal big data. Sensors, 18.","DOI":"10.3390\/s18030684"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Celli, F., and Lepri, B. (2018, January 10\u201312). Is Big Five better than MBTI ? A personality computing challenge using Twitter data. Proceedings of the CEUR Workshop, Torino, Italy.","DOI":"10.4000\/books.aaccademia.3147"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Callejas-Cuervo, M., Mart\u00ednez-Tejada, L.A., and Botero-Fagua, J.A. (2017, January 26\u201328). Architecture of an emotion recognition and video games system to identify personality traits. Proceedings of the VII Latin American Congress on Biomedical Engineering CLAIB 2016, Bucaramanga, Santander, Colombia.","DOI":"10.1007\/978-981-10-4086-3_11"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hu, K., Guo, S., Pang, N., and Wang, H. (2007, January 15\u201318). An intensity-based personalized affective model. Proceedings of the 2007 IEEE International Conference on Robotics and Biomimetics, ROBIO, Sanya, China.","DOI":"10.1109\/ROBIO.2007.4522513"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1109\/TAFFC.2017.2762299","article-title":"Deep Bimodal Regression of Apparent Personality Traits from Short Video Sequences","volume":"9","author":"Wei","year":"2018","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3817","DOI":"10.1016\/j.ins.2010.06.034","article-title":"Affectively intelligent and adaptive car interfaces","volume":"180","author":"Nasoz","year":"2010","journal-title":"Inf. Sci. NY"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1007\/s12369-017-0399-6","article-title":"Automated Prediction of Extraversion During Human\u2013Humanoid Interaction","volume":"9","author":"Anzalone","year":"2017","journal-title":"Int. J. Soc. Robot."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Bhin, H., Lim, Y., Park, S., and Choi, J. (July, January 28). Automated psychophysical personality data acquisition system for human-robot interaction. Proceedings of the 2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2017, Jeju, Korea.","DOI":"10.1109\/URAI.2017.7992699"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Cai, R., Guo, A., Ma, J., Huang, R., Yu, R., and Yang, C. (2018, January 12\u201315). Correlation Analyses Between Personality Traits and Personal Behaviors Under Specific Emotion States Using Physiological Data from Wearable Devices. Proceedings of the 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC\/PiCom\/DataCom\/CyberSciTech), Athens, Greece.","DOI":"10.1109\/DASC\/PiCom\/DataCom\/CyberSciTec.2018.00023"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Miranda-Correa, J.A., and Patras, I. (2018, January 15\u201319). A Multi-Task Cascaded Network for Prediction of Affect, Personality, Mood and Social Context Using EEG Signals. Proceedings of the 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), Xi\u2019an, China.","DOI":"10.1109\/FG.2018.00060"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/s00406-010-0127-9","article-title":"Attention to emotion: Auditory-evoked potentials in an emotional choice reaction task and personality traits as assessed by the NEO FFI","volume":"261","author":"Mittermeier","year":"2011","journal-title":"Eur. Arch. Psychiatry Clin. Neurosci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1109\/TAFFC.2016.2625250","article-title":"Ascertain: Emotion and personality recognition using commercial sensors","volume":"9","author":"Subramanian","year":"2018","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"489","DOI":"10.3758\/s13415-016-0408-5","article-title":"Individual differences in emotion word processing: A diffusion model analysis","volume":"16","author":"Mueller","year":"2016","journal-title":"Cogn. Affect. Behav. Neurosci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1177\/0146167200268008","article-title":"Action, Emotion, and Personality: Emerging Conceptual Integration","volume":"26","author":"Carver","year":"2000","journal-title":"Personal. Soc. Psychol. Bull."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Allers, R. (1961). Emotion and Personality, Columbia University Press.","DOI":"10.5840\/newscholas196135345"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1111\/j.1467-6494.2004.00298.x","article-title":"Healthy and Unhealthy Emotion Regulation: Personality Processes, Individual Differences, and Life Span Development","volume":"72","author":"John","year":"2004","journal-title":"J. Pers."},{"key":"ref_51","unstructured":"Miranda Correa, J.A., Abadi, M.K., Sebe, N., and Patras, I. (2018). AMIGOS: A Dataset for Affect, Personality and Mood Research on Individuals and Groups. IEEE Trans. Affective Comput., 1."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1109\/TAMD.2015.2431497","article-title":"Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks","volume":"7","author":"Zheng","year":"2015","journal-title":"IEEE Trans. Auton. Ment. Dev."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1109\/T-AFFC.2010.12","article-title":"Optimal arousal identification and classification for affective computing using physiological signals: Virtual reality stroop task","volume":"1","author":"Wu","year":"2010","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/T-AFFC.2011.15","article-title":"DEAP: A Database for Emotion Analysis using Physiological Signals","volume":"3","author":"Koelstra","year":"2012","journal-title":"IEEE Trans. Affective Comput."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1110","DOI":"10.1109\/TCYB.2018.2797176","article-title":"EmotionMeter: A Multimodal Framework for Recognizing Human Emotions","volume":"49","author":"Zheng","year":"2019","journal-title":"IEEE Trans. Cybern."},{"key":"ref_56","unstructured":"Raad, B.D., and Perugini, M. (2002). Big Five Assessment, Hogrefe & Huber Publishers."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"162","DOI":"10.3389\/fnins.2018.00162","article-title":"Exploring EEG Features in Cross-Subject Emotion Recognition","volume":"12","author":"Li","year":"2018","journal-title":"Front. Neurosci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1109\/TAFFC.2014.2339834","article-title":"Feature Extraction and Selection for Emotion Recognition from EEG","volume":"5","author":"Jenke","year":"2014","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_59","unstructured":"Becker, H., Fleureau, J., Guillotel, P., Wendling, F., Merlet, I., and Albera, L. (2017). Emotion recognition based on high-resolution EEG recordings and reconstructed brain sources. IEEE Trans. Affect. Comput."},{"key":"ref_60","unstructured":"Sourina, O., and Liu, Y. (2011, January 26\u201329). A Fractal-based Algorithm of Emotion Recognition from EEG using Arousal-Valence Model. Proceedings of the BIOSIGNALS International Conference on Bio-Inspired Systems and Signal, Rome, Italy."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Chen, D.-W., Miao, R., Yang, W.-Q., Liang, Y., Chen, H.-H., Huang, L., Deng, C.-J., and Han, N. (2019). A Feature Extraction Method Based on Differential Entropy and Linear Discriminant Analysis for Emotion Recognition. Sensors, 19.","DOI":"10.3390\/s19071631"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Duan, R.-N., Zhu, J.-Y., and Lu, B.-L. (2013, January 6\u20138). Differential entropy feature for EEG-based emotion classification. Proceedings of the 2013 6th International IEEE\/EMBS Conference on Neural Engineering (NER), San Diego, CA, USA.","DOI":"10.1109\/NER.2013.6695876"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/0005-7916(94)90063-9","article-title":"Measuring emotion: The self-assessment manikin and the semantic differential","volume":"25","author":"Bradley","year":"1994","journal-title":"J. Behav. Ther. Exp. Psychiatry"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Guyon, I. (2006). Feature Extraction Foundations and Applications, Springer.","DOI":"10.1007\/978-3-540-35488-8"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1093\/bioinformatics\/btm344","article-title":"A review of feature selection techniques in bioinformatics","volume":"23","author":"Saeys","year":"2007","journal-title":"Bioinformatics"},{"key":"ref_66","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_67","unstructured":"Huan, L., and Hiroshi, M. (2007). Computational Methods of Feature Selection, CRC Press. [1st ed.]."},{"key":"ref_68","unstructured":"Boschetti, A., and Massaron, L. (2016). Python Data Science Essentials, Packt Publishing. [2nd ed.]."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Zhao, S., Ding, G., Han, J., and Gao, Y. (2018, January 13\u201319). Personality-aware personalized emotion recognition from physiological signals. Proceedings of the IJCAI International Joint Conference on Artificial Intelligence, Stockholm, Sweden.","DOI":"10.24963\/ijcai.2018\/230"}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/2\/2\/7\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:44:53Z","timestamp":1760363093000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/2\/2\/7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,13]]},"references-count":69,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["make2020007"],"URL":"https:\/\/doi.org\/10.3390\/make2020007","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,13]]}}}