{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T14:28:21Z","timestamp":1768832901968,"version":"3.49.0"},"reference-count":46,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,12,3]],"date-time":"2018-12-03T00:00:00Z","timestamp":1543795200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>First, the Likert scale and self-assessment manikin are used to provide emotion analogies, but they have limits for reflecting subjective factors. To solve this problem, we use physiological signals that show objective responses from cognitive status. The physiological signals used are electrocardiogram, skin temperature, and electrodermal activity (EDA). Second, the degree of emotion felt, and the related physiological signals, vary according to the individual. KLD calculates the difference in probability distribution shape patterns between two classes. Therefore, it is possible to analyze the relationship between physiological signals and emotion. As the result, features from EDA are important for distinguishing negative emotion in all subjects. In addition, the proposed feature selection algorithm showed an average accuracy of 92.5% and made it possible to improve the accuracy of negative emotion recognition.<\/jats:p>","DOI":"10.3390\/s18124253","type":"journal-article","created":{"date-parts":[[2018,12,4]],"date-time":"2018-12-04T03:01:37Z","timestamp":1543892497000},"page":"4253","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Design of User-Customized Negative Emotion Classifier Based on Feature Selection Using Physiological Signal Sensors"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5177-9152","authenticated-orcid":false,"given":"JeeEun","family":"Lee","sequence":"first","affiliation":[{"name":"Graduate Program of Biomedical Engineering, Yonsei University, Seoul 03722, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6032-4686","authenticated-orcid":false,"given":"Sun K.","family":"Yoo","sequence":"additional","affiliation":[{"name":"Department of Medical Engineering, Yonsei University College of Medicine, Seoul 03722, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1080\/09658211.2012.704049","article-title":"Differential effects of arousal in positive and negative autobiographical memories","volume":"20","author":"Ford","year":"2012","journal-title":"Memory"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/79.911197","article-title":"Emotion recognition in human-computer interaction","volume":"18","author":"Cowie","year":"2001","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1146\/annurev.psych.60.110707.163539","article-title":"Emotion theory and research: Highlights, unanswered questions, and emerging issues","volume":"60","author":"Carroll","year":"2009","journal-title":"Annu. Rev. Psychol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"7120","DOI":"10.3390\/s140407120","article-title":"Wearable biomedical measurement systems for assessment of mental stress of combatants in real time","volume":"14","author":"Seoane","year":"2014","journal-title":"Sensors"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Jerritta, S., Murugappan, M., Nagarajan, R., and Wan, K. (2011, January 4\u20136). Physiological signals based human emotion recognition: A review. Proceedings of the 2011 IEEE 7th International Colloquium on Signal Processing and Its Applications (CSPA), Penang, Malaysia.","DOI":"10.1109\/CSPA.2011.5759912"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.biopsycho.2004.03.002","article-title":"Frontal EEG asymmetry as a moderator and mediator of emotion","volume":"67","author":"Coan","year":"2004","journal-title":"Boil. Psychol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Aric\u00f2, P., Borghini, G., Di Flumeri, G., and Sciaraffa, N. (2018). Passive BCI beyond the lab: Current trends and future directions. Physiol. Meas., 39.","DOI":"10.1088\/1361-6579\/aad57e"},{"key":"ref_8","unstructured":"Zhang, J., Chen, M., Hu, S., Cao, Y., and Kozma, R. (2016, January 9\u201312). PNN for EEG-based Emotion Recognition. Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Budapest, Hungary."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"11449","DOI":"10.1007\/s11042-016-4203-7","article-title":"Development of emotion recognition interface using complex EEG\/ECG bio-signal for interactive contents","volume":"76","author":"Shin","year":"2017","journal-title":"Multimedia Tools Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1109\/TAFFC.2014.2327617","article-title":"Emotion recognition based on multi-variant correlation of physiological signals","volume":"5","author":"Wen","year":"2014","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Mirmohamadsadeghi, L., Yazdani, A., and Vesin, J.M. (2016, January 21\u201323). Using cardio-respiratory signals to recognize emotions elicited by watching music video clips. Proceedings of the 2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP), Montreal, QC, Canada.","DOI":"10.1109\/MMSP.2016.7813349"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, X., Song, D., Zhang, P., and Yu, G. (2016, January 15\u201318). Emotion recognition from multi-channel EEG data through Convolutional Recurrent Neural Network. Proceedings of the 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Shenzhen, China.","DOI":"10.1109\/BIBM.2016.7822545"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liu, J., Meng, H., Nandi, A., and Li, M. (2016, January 13\u201315). Emotion detection from EEG recordings. Proceedings of the 2016 12nd International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Changsha, China.","DOI":"10.1109\/FSKD.2016.7603437"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Garc\u00eda, H.F., \u00c1lvarez, M.A., and Orozco, \u00c1.A. (2016, January 17\u201320). Gaussian process dynamical models for multimodal affect recognition. Proceedings of the 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7590834"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Guo, H.W., Huang, Y.S., Lin, C.H., Chien, J.C., Haraikawa, K., and Shieh, J.S. (November, January 31). Heart Rate Variability Signal Features for Emotion Recognition by Using Principal Component Analysis and Support Vectors Machine. Proceedings of the 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE), Taichung, Taiwan.","DOI":"10.1109\/BIBE.2016.40"},{"key":"ref_16","unstructured":"Zheng, W.-L., Zhu, J.-Y., and Lu, B.-L. (2017). Identifying stable patterns over time for emotion recognition from EEG. IEEE Trans. Affect. Comput."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","unstructured":"Shu, L., Xie, J., Yang, M., Li, Z., Li, Z., Liao, D., Xu, X., and Yang, X. (2018). A review of emotion recognition using physiological signals. Sensors, 18.","DOI":"10.3390\/s18072074"},{"key":"ref_19","unstructured":"Benovoy, M., Cooperstock, J.R., and Deitcher, J. (2008, January 28\u201331). Biosignals analysis and its application in a performance setting. Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing, Madeira, Portugal."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1023\/B:AIRE.0000045502.10941.a9","article-title":"A Survey of Outlier Detection Methodologies","volume":"22","author":"Hodge","year":"2004","journal-title":"Artif. Intell. Rev."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"25607","DOI":"10.3390\/s151025607","article-title":"Assessment of mental, emotional and physical stress through analysis of physiological signals using smartphones","volume":"15","author":"Ferreira","year":"2015","journal-title":"Sensors"},{"key":"ref_22","unstructured":"Wong, W.M., Tan, A.W., Loo, C.K., and Liew, W.S. (2010, January 15\u201317). PSO optimization of synergetic neural classifier for multichannel emotion recognition. Proceedings of the 2010 Second World Congress on Nature and Biologically Inspired Computing (NaBIC), Fukuoka, Japan."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, Y., and Mo, J. (2013, January 25\u201327). Emotion feature selection from physiological signals using tabu search. Proceedings of the 2013 25th Chinese Control and Decision Conference (CCDC), Guiyang, China.","DOI":"10.1109\/CCDC.2013.6561487"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"031005","DOI":"10.1088\/1741-2552\/aab2f2","article-title":"A review of classification algorithms for EEG-based brain\u2013computer interfaces: A 10 year update","volume":"15","author":"Lotte","year":"2018","journal-title":"J. Neural Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1109\/TBME.2017.2694856","article-title":"Passive BCI in operational environments: Insights, recent advances, and future trends","volume":"64","author":"Borghini","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, W., Zheng, W.-L., and Lu, B.-L. (2016, January 16\u201321). Emotion recognition using multimodal deep learning. Proceedings of the International Conference on Neural Information Processing, Kyoto, Japan.","DOI":"10.1007\/978-3-319-46672-9_58"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kawde, P., and Verma, G.K. (2017, January 26\u201328). Deep belief network based affect recognition from physiological signals. Proceedings of the 2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON), Mathura, India.","DOI":"10.1109\/UPCON.2017.8251115"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hershey, J.R., and Olsen, P.A. (2007, January 15\u201320). Approximating the Kullback Leibler divergence between Gaussian mixture models. Proceedings of the ICASSP 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, Honolulu, HI, USA.","DOI":"10.1109\/ICASSP.2007.366913"},{"key":"ref_29","unstructured":"Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y. (2016). Deep Learning, MIT Press."},{"key":"ref_30","unstructured":"Han, J., Pei, J., and Kamber, M. (2011). Data Mining: Concepts and Techniques, Elsevier."},{"key":"ref_31","first-page":"275","article-title":"Galvanic skin response in mood disorders: A critical review","volume":"15","author":"Rodrigo","year":"2015","journal-title":"Int. J. Psychol. Psychol. Ther."},{"key":"ref_32","unstructured":"Choi, W. (2011). A Classification Analysis of Negative Emotion Based on PPG Signal Using Fuzzy-Ga, Yonsei University."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1037\/1089-2680.10.3.229","article-title":"Heart rate variability as an index of regulated emotional responding","volume":"10","author":"Appelhans","year":"2006","journal-title":"Rev. Gen. Psychol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1093\/oxfordjournals.eurheartj.a014868","article-title":"Heart rate variability: Standards of measurement, physiological interpretation, and clinical use","volume":"17","author":"Malik","year":"1996","journal-title":"Eur. Heart J."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Acharya, R., Krishnan, S.M., Spaan, J.A., and Suri, J.S. (2007). Heart rate variability. Advances in Cardiac Signal Processing, Springer.","DOI":"10.1007\/978-3-540-36675-1"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.ijpsycho.2005.10.024","article-title":"Basic emotions are associated with distinct patterns of cardiorespiratory activity","volume":"61","author":"Rainville","year":"2006","journal-title":"Int. J. Psychophysiol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.1109\/IEMBS.2006.259421","article-title":"Stress Detection in Computer Users based on Digital Signal Processing of Noninvasive Physiological Variables","volume":"1","author":"Zhai","year":"2006","journal-title":"Conf. Proc. IEEE Eng. Med. Biol. Soc."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1037\/h0023544","article-title":"Fear and Autonomic Arousal","volume":"71","author":"Geer","year":"1966","journal-title":"J. Abnorm. Psychol."},{"key":"ref_39","unstructured":"Berridge, K.C. (1999). Pleasure, Pain, Desire, and Dread: Hidden Core Processes of Emotion, Russell Sage Foundation."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1109\/TSMCA.2012.2210408","article-title":"Emotional State Classification in Patient\u2013Robot Interaction using Wavelet Analysis and Statistics-based Feature Selection","volume":"43","author":"Swangnetr","year":"2013","journal-title":"IEEE Trans. Hum.-Mach. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.neunet.2015.07.005","article-title":"Georgieva, Learning to Decode Human Emotions with Echo State Networks","volume":"78","author":"Bozhkov","year":"2016","journal-title":"Neural Netw."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1109\/TSMCA.2008.918624","article-title":"Toward Emotion Recognition in Car-Racing Drivers: A Biosignal Processing Approach","volume":"30","author":"Katsis","year":"2008","journal-title":"IEEE Trans. Syst. Man Cybern. -Part A Syst. Hum."},{"key":"ref_43","first-page":"1089","article-title":"No unbiased estimator of the variance of k-fold cross-validation","volume":"5","author":"Bengio","year":"2004","journal-title":"J. Mach. Learn. Res."},{"key":"ref_44","unstructured":"Abu-Mostafa, Y.S., Magdon-Ismail, M., and Lin, H.-T. (2012). Learning from Data, AMLBook."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1111\/j.1469-8986.2012.01483.x","article-title":"Model-based analysis of skin conductance responses: Towards causal models in psychophysiology","volume":"50","author":"Bach","year":"2013","journal-title":"Psychophysiology"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2067","DOI":"10.1109\/TPAMI.2008.26","article-title":"Emotion recognition based on physiological changes in music listening","volume":"30","author":"Kim","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/12\/4253\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:30:55Z","timestamp":1760196655000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/12\/4253"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12,3]]},"references-count":46,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2018,12]]}},"alternative-id":["s18124253"],"URL":"https:\/\/doi.org\/10.3390\/s18124253","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,12,3]]}}}