{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T18:42:18Z","timestamp":1764873738704,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,5,27]],"date-time":"2020-05-27T00:00:00Z","timestamp":1590537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2019A1515011375"],"award-info":[{"award-number":["2019A1515011375"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009334","name":"Pearl River S and T Nova Program of Guangzhou","doi-asserted-by":"publisher","award":["201710010038"],"award-info":[{"award-number":["201710010038"]}],"id":[{"id":"10.13039\/501100009334","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61876067"],"award-info":[{"award-number":["61876067"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Electroencephalogram (EEG) signals have been widely used in emotion recognition. However, the current EEG-based emotion recognition has low accuracy of emotion classification, and its real-time application is limited. In order to address these issues, in this paper, we proposed an improved feature selection algorithm to recognize subjects\u2019 emotion states based on EEG signal, and combined this feature selection method to design an online emotion recognition brain-computer interface (BCI) system. Specifically, first, different dimensional features from the time-domain, frequency domain, and time-frequency domain were extracted. Then, a modified particle swarm optimization (PSO) method with multi-stage linearly-decreasing inertia weight (MLDW) was purposed for feature selection. The MLDW algorithm can be used to easily refine the process of decreasing the inertia weight. Finally, the emotion types were classified by the support vector machine classifier. We extracted different features from the EEG data in the DEAP data set collected by 32 subjects to perform two offline experiments. Our results showed that the average accuracy of four-class emotion recognition reached 76.67%. Compared with the latest benchmark, our proposed MLDW-PSO feature selection improves the accuracy of EEG-based emotion recognition. To further validate the efficiency of the MLDW-PSO feature selection method, we developed an online two-class emotion recognition system evoked by Chinese videos, which achieved good performance for 10 healthy subjects with an average accuracy of 89.5%. The effectiveness of our method was thus demonstrated.<\/jats:p>","DOI":"10.3390\/s20113028","type":"journal-article","created":{"date-parts":[[2020,5,28]],"date-time":"2020-05-28T12:36:58Z","timestamp":1590669418000},"page":"3028","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Enhancing BCI-Based Emotion Recognition Using an Improved Particle Swarm Optimization for Feature Selection"],"prefix":"10.3390","volume":"20","author":[{"given":"Zina","family":"Li","sequence":"first","affiliation":[{"name":"School of Software, South China Normal University, Guangzhou 510631, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lina","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Software, South China Normal University, Guangzhou 510631, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruixin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Software, South China Normal University, Guangzhou 510631, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6226-754X","authenticated-orcid":false,"given":"Zhipeng","family":"He","sequence":"additional","affiliation":[{"name":"School of Software, South China Normal University, Guangzhou 510631, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Software, South China Normal University, Guangzhou 510631, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1669-8338","authenticated-orcid":false,"given":"Fei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Software, South China Normal University, Guangzhou 510631, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiahui","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Software, South China Normal University, Guangzhou 510631, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.inffus.2018.09.008","article-title":"Emotion recognition using deep learning approach from audio\u2013visual emotional big data","volume":"49","author":"Hossain","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1017\/S0954579405050340","article-title":"The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology","volume":"17","author":"Posner","year":"2005","journal-title":"Dev. Psychopathol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/0013-4694(70)90143-4","article-title":"EEG analysis based on time domain properties","volume":"29","author":"Hjorth","year":"1970","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liu, Y., and Sourina, O. (2013). Real-time fractal-based valence level recognition from EEG. Transactions on Computational Science XVIII, Springer.","DOI":"10.1007\/978-3-642-38803-3_6"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Hosseini, S.A., Khalilzadeh, M.A., Naghibi-Sistani, M.B., and Niazmand, V. (2010, January 24\u201325). Higher order spectra analysis of EEG signals in emotional stress states. Proceedings of the 2010 Second International Conference on Information Technology and Computer Science, Kiev, Ukraine.","DOI":"10.1109\/ITCS.2010.21"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1587\/transinf.2015EDP7251","article-title":"Continuous music-emotion recognition based on electroencephalogram","volume":"99","author":"Thammasan","year":"2016","journal-title":"IEICE Trans. Inf. Syst."},{"key":"ref_7","unstructured":"Shi, L.-C., Jiao, Y.-Y., and Lu, B.-L. (2013, January 3\u20137). Differential entropy feature for EEG-based vigilance estimation. Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1109\/TAFFC.2017.2712143","article-title":"Identifying stable patterns over time for emotion recognition from EEG","volume":"10","author":"Zheng","year":"2017","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Jahankhani, P., Kodogiannis, V., and Revett, K. (2006, January 3\u20136). EEG signal classification using wavelet feature extraction and neural networks. Proceedings of the IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing (JVA\u201906), Sofia, Bulgaria.","DOI":"10.1109\/JVA.2006.17"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Samara, A., Menezes, M.L.R., and Galway, L. (2016, January 14\u201316). Feature extraction for emotion recognition and modelling using neurophysiological data. Proceedings of the 2016 15th international conference on ubiquitous computing and communications and 2016 international symposium on cyberspace and security (IUCC-CSS), Granada, Spain.","DOI":"10.1109\/IUCC-CSS.2016.027"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Liu, Y., and Sourina, O. (2014). Real-time subject-dependent EEG-based emotion recognition algorithm. Transactions on Computational Science XXIII, Springer.","DOI":"10.1109\/SMC.2014.6974415"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.neucom.2017.03.027","article-title":"Fusing highly dimensional energy and connectivity features to identify affective states from EEG signals","volume":"244","author":"Ramzan","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"19","DOI":"10.3389\/fnbot.2017.00019","article-title":"Cross-subject EEG feature selection for emotion recognition using transfer recursive feature elimination","volume":"11","author":"Yin","year":"2017","journal-title":"Front. Neurorobotics"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.cmpb.2013.10.007","article-title":"Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis. Comput","volume":"113","author":"Inbarani","year":"2014","journal-title":"Methods Programs Biomed."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3239","DOI":"10.1007\/s00521-016-2236-5","article-title":"PSO-based feature selection and neighborhood rough set-based classification for BCI multiclass motor imagery task","volume":"28","author":"Kumar","year":"2017","journal-title":"Neural Comput. Appl."},{"key":"ref_16","unstructured":"Shi, Y., and Eberhart, R.C. (1999, January 6\u20139). Empirical study of particle swarm optimization. Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406), Washington, DC, USA."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/s12193-011-0080-6","article-title":"Real-time EEG-based emotion recognition for music therapy","volume":"5","author":"Sourina","year":"2012","journal-title":"J. Multimodal User Interfaces"},{"key":"ref_18","unstructured":"Pan, J., Li, Y., and Wang, J. (2016, January 24\u201329). An EEG-based brain-computer interface for emotion recognition. Proceedings of the 2016 international joint conference on neural networks (IJCNN), Vancouver, BC, Canada."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"046022","DOI":"10.1088\/1741-2560\/13\/4\/046022","article-title":"Affective brain\u2013computer music interfacing","volume":"13","author":"Daly","year":"2016","journal-title":"J. Neural Eng."},{"key":"ref_20","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":"2011","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/T-AFFC.2011.25","article-title":"A multimodal database for affect recognition and implicit tagging","volume":"3","author":"Soleymani","year":"2011","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1037\/a0030811","article-title":"The relation between valence and arousal in subjective experience","volume":"139","author":"Kuppens","year":"2013","journal-title":"Psychol. Bull."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/34.192463","article-title":"A theory for multiresolution signal decomposition: The wavelet representation","volume":"11","author":"Mallat","year":"1989","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bratton, D., and Kennedy, J. (2007, January 1\u20135). Defining a Standard for Particle Swarm Optimization. Proceedings of the 2007 IEEE Swarm Intelligence Symposium, Honolulu, HI, USA.","DOI":"10.1109\/SIS.2007.368035"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1016\/S0141-9331(02)00053-4","article-title":"Particle swarm optimization for task assignment problem","volume":"26","author":"Salman","year":"2002","journal-title":"Microprocess. Microsyst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Xin, J., Chen, G., and Hai, Y. (2009, January 24\u201326). A particle swarm optimizer with multi-stage linearly-decreasing inertia weight. Proceedings of the 2009 International Joint Conference on Computational Sciences and Optimization, Sanya, China.","DOI":"10.1109\/CSO.2009.420"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"2604","DOI":"10.1109\/TSP.2012.2187647","article-title":"Adaptive emotional information retrieval from EEG signals in the time-frequency domain","volume":"60","author":"Petrantonakis","year":"2012","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wagh, K.P., and Vasanth, K. (2019). Electroencephalograph (EEG) Based Emotion Recognition System: A Review. Innovations in Electronics and Communication Engineering (Lecture Notes in Networks and Systems), Springer.","DOI":"10.1007\/978-981-10-8204-7_5"},{"key":"ref_33","unstructured":"Chen, J., Hu, B., Wang, Y., Dai, Y., Yao, Y., and Zhao, S. (2016, January 15\u201318). A three-stage decision framework for multi-subject emotion recognition using physiological signals. Proceedings of the 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Shenzhen, China."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2266","DOI":"10.1109\/JSEN.2018.2883497","article-title":"Cross-subject emotion recognition using flexible analytic wavelet transform from EEG signals","volume":"19","author":"Gupta","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1109\/TAFFC.2017.2660485","article-title":"Real-time movie-induced discrete emotion recognition from EEG signals","volume":"9","author":"Liu","year":"2017","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhuang, N., Zeng, Y., Yang, K., Zhang, C., Tong, L., and Yan, B. (2018). Investigating patterns for self-induced emotion recognition from EEG signals. Sensors, 18.","DOI":"10.3390\/s18030841"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Jatupaiboon, N., Pan-ngum, S., and Israsena, P. (2013). Real-time EEG-based happiness detection system. Sci. World J., 2013.","DOI":"10.1155\/2013\/618649"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3498","DOI":"10.1109\/TBME.2012.2217495","article-title":"Toward an EEG-based recognition of music liking using time-frequency analysis","volume":"59","author":"Hadjidimitriou","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/11\/3028\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:32:56Z","timestamp":1760175176000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/11\/3028"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,27]]},"references-count":38,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["s20113028"],"URL":"https:\/\/doi.org\/10.3390\/s20113028","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,5,27]]}}}