{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T09:23:26Z","timestamp":1781342606693,"version":"3.54.1"},"reference-count":43,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,10]],"date-time":"2021-02-10T00:00:00Z","timestamp":1612915200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFE0118200"],"award-info":[{"award-number":["2017YFE0118200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFE0116800"],"award-info":[{"award-number":["2017YFE0116800"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61971173"],"award-info":[{"award-number":["61971173"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61671193"],"award-info":[{"award-number":["61671193"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1909202"],"award-info":[{"award-number":["U1909202"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017599","name":"Science and Technology Program of Zhejiang Province","doi-asserted-by":"publisher","award":["2018C04012"],"award-info":[{"award-number":["2018C04012"]}],"id":[{"id":"10.13039\/501100017599","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LY21F030005"],"award-info":[{"award-number":["LY21F030005"]}]},{"name":"Fundamental Research Funds for the Provincial Universities of Zhejiang","award":["GK209907299001-008"],"award-info":[{"award-number":["GK209907299001-008"]}]},{"name":"Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment of Ministry of Education, Anhui Polytechnic University","award":["GDSC202015"],"award-info":[{"award-number":["GDSC202015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Emotion recognition has a wide range of potential applications in the real world. Among the emotion recognition data sources, electroencephalography (EEG) signals can record the neural activities across the human brain, providing us a reliable way to recognize the emotional states. Most of existing EEG-based emotion recognition studies directly concatenated features extracted from all EEG frequency bands for emotion classification. This way assumes that all frequency bands share the same importance by default; however, it cannot always obtain the optimal performance. In this paper, we present a novel multi-scale frequency bands ensemble learning (MSFBEL) method to perform emotion recognition from EEG signals. Concretely, we first re-organize all frequency bands into several local scales and one global scale. Then we train a base classifier on each scale. Finally we fuse the results of all scales by designing an adaptive weight learning method which automatically assigns larger weights to more important scales to further improve the performance. The proposed method is validated on two public data sets. For the \u201cSEED IV\u201d data set, MSFBEL achieves average accuracies of 82.75%, 87.87%, and 78.27% on the three sessions under the within-session experimental paradigm. For the \u201cDEAP\u201d data set, it obtains average accuracy of 74.22% for four-category classification under 5-fold cross validation. The experimental results demonstrate that the scale of frequency bands influences the emotion recognition rate, while the global scale that directly concatenating all frequency bands cannot always guarantee to obtain the best emotion recognition performance. Different scales provide complementary information to each other, and the proposed adaptive weight learning method can effectively fuse them to further enhance the performance.<\/jats:p>","DOI":"10.3390\/s21041262","type":"journal-article","created":{"date-parts":[[2021,2,12]],"date-time":"2021-02-12T16:12:10Z","timestamp":1613146330000},"page":"1262","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Multi-Scale Frequency Bands Ensemble Learning for EEG-Based Emotion Recognition"],"prefix":"10.3390","volume":"21","author":[{"given":"Fangyao","family":"Shen","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1208-972X","authenticated-orcid":false,"given":"Yong","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China"},{"name":"MoE Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment, Anhui Polytechnic University, Wuhu 241000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0113-6968","authenticated-orcid":false,"given":"Wanzeng","family":"Kong","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China"},{"name":"Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guojun","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China"},{"name":"Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,10]]},"reference":[{"key":"ref_1","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_2","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":"2012","journal-title":"IEEE Trans. Hum. Mach. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1109\/MCI.2020.2998234","article-title":"Improving depression level estimation by concurrently learning emotion intensity","volume":"15","author":"Qureshi","year":"2020","journal-title":"IEEE Comput. Intell. Mag."},{"key":"ref_4","first-page":"1","article-title":"Emotion regulating attentional control abnormalities in major depressive disorder: An event-related potential study","volume":"7","author":"Hu","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.procs.2017.12.003","article-title":"An emotion recognition model based on facial recognition in virtual learning environment","volume":"125","author":"Yang","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, T.M., Shen, W.X., Chao, H.C., and Zeadally, S. (2019). Analysis of Students\u2019 Learning Emotions Using EEG. International Conference on Innovative Technologies and Learning, Springer.","DOI":"10.1007\/978-3-030-35343-8_53"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zhao, S., Yang, G., Tang, J., Zhang, T., Peng, Y., and Kong, W. (2018, January 3\u20136). Emotional-state brain network analysis revealed by minimum spanning tree using EEG signals. Proceedings of the 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain.","DOI":"10.1109\/BIBM.2018.8621497"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"106756","DOI":"10.1016\/j.asoc.2020.106756","article-title":"A joint optimization framework to semi-supervised RVFL and ELM networks for efficient data classification","volume":"97","author":"Peng","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wu, S., Xu, X., Shu, L., and Hu, B. (2017, January 13\u201316). Estimation of valence of emotion using two frontal EEG channels. Proceedings of the 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, USA.","DOI":"10.1109\/BIBM.2017.8217815"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1109\/TAFFC.2017.2714671","article-title":"Emotions recognition using EEG signals: A survey","volume":"10","author":"Alarcao","year":"2017","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_11","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_12","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_13","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1109\/TITB.2009.2034649","article-title":"Emotion recognition from EEG using higher order crossings","volume":"14","author":"Petrantonakis","year":"2009","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1109\/TITB.2010.2041553","article-title":"Toward emotion aware computing: An integrated approach using multichannel neurophysiological recordings and affective visual stimuli","volume":"14","author":"Frantzidis","year":"2010","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TBME.2010.2048568","article-title":"EEG-based emotion recognition in music listening","volume":"57","author":"Lin","year":"2010","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","unstructured":"Paszkiel, S. (2020). Using neural networks for classification of the changes in the EEG signal based on facial expressions. Analysis and Classification of EEG Signals for Brain\u2013Computer Interfaces, Springer.","DOI":"10.1007\/978-3-030-30581-9_7"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"948","DOI":"10.1166\/jmihi.2021.3340","article-title":"Design and Implementation of Human-Computer Interaction Systems Based on Transfer Support Vector Machine and EEG Signal for Depression Patients\u2019 Emotion Recognition","volume":"11","author":"Chen","year":"2021","journal-title":"J. Med. Imaging Health Inform."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.neucom.2015.03.118","article-title":"Discriminative manifold extreme learning machine and applications to image and EEG signal classification","volume":"174","author":"Peng","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1007\/s12559-017-9533-x","article-title":"Hierarchical convolutional neural networks for EEG-based emotion recognition","volume":"10","author":"Li","year":"2018","journal-title":"Cogn. Comput."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","unstructured":"Yang, Y., Wu, Q., Fu, Y., and Chen, X. (2018). Continuous convolutional neural network with 3d input for eeg-based emotion recognition. International Conference on Neural Information Processing, Springer.","DOI":"10.1007\/978-3-030-04239-4_39"},{"key":"ref_24","unstructured":"Lin, D., and Tang, X. (2006, January 17\u201322). Recognize high resolution faces: From macrocosm to microcosm. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201906), New York, NY, USA."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.inffus.2017.02.004","article-title":"Ensemble learning for data stream analysis: A survey","volume":"37","author":"Krawczyk","year":"2017","journal-title":"Inf. Fusion"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Dietterich, T.G. (2000). Ensemble methods in machine learning. International Workshop on Multiple Classifier Systems, Springer.","DOI":"10.1007\/3-540-45014-9_1"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"296","DOI":"10.2174\/157489310794072508","article-title":"A review of ensemble methods in bioinformatics","volume":"5","author":"Yang","year":"2010","journal-title":"Curr. Bioinform."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kumar, R., Banerjee, A., and Vemuri, B.C. (2009, January 20\u201325). Volterrafaces: Discriminant analysis using volterra kernels. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPRW.2009.5206837"},{"key":"ref_29","unstructured":"Zhang, L., Yang, M., and Feng, X. (2011, January 6\u201313). Sparse representation or collaborative representation: Which helps face recognition?. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain."},{"key":"ref_30","first-page":"941","article-title":"Boosting as a regularized path to a maximum margin classifier","volume":"5","author":"Rosset","year":"2004","journal-title":"J. Mach. Learn. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1109\/TNN.2010.2040484","article-title":"Boosting through optimization of margin distributions","volume":"21","author":"Shen","year":"2010","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_32","unstructured":"Bonnans, J.F., Gilbert, J.C., Lemar\u00e9chal, C., and Sagastiz\u00e1bal, C.A. (2006). Numerical Optimization: Theoretical and Practical Aspects, Springer Science & Business Media."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2428","DOI":"10.1109\/TIP.2018.2886761","article-title":"A general framework for auto-weighted feature selection via global redundancy minimization","volume":"28","author":"Nie","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_34","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":"2018","journal-title":"IEEE Trans. Cybern."},{"key":"ref_35","unstructured":"Shi, L.C., and Lu, B.L. (September, January 31). Off-line and on-line vigilance estimation based on linear dynamical system and manifold learning. Proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, Argentina."},{"key":"ref_36","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_37","doi-asserted-by":"crossref","unstructured":"Yang, Y., Wu, Q., Qiu, M., Wang, Y., and Chen, X. (2018, January 8\u201313). Emotion recognition from multi-channel EEG through parallel convolutional recurrent neural network. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489331"},{"key":"ref_38","first-page":"3281","article-title":"Multisource transfer learning for cross-subject EEG emotion recognition","volume":"50","author":"Li","year":"2019","journal-title":"IEEE Trans. Cybernet."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1080\/01621459.1937.10503522","article-title":"The use of ranks to avoid the assumption of normality implicit in the analysis of variance","volume":"32","author":"Friedman","year":"1937","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_40","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref_41","unstructured":"Kyrillidis, A., Becker, S., Cevher, V., and Koch, C. (2013, January 16\u201321). Sparse projections onto the simplex. Proceedings of the International Conference on Machine Learning, Atlanta, GA, USA."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Boyd, S., Boyd, S.P., and Vandenberghe, L. (2004). Convex Optimization, Cambridge University Press.","DOI":"10.1017\/CBO9780511804441"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1137\/0715050","article-title":"On Newton-iterative methods for the solution of systems of nonlinear equations","volume":"15","author":"Sherman","year":"1978","journal-title":"SIAM J. Numer. Anal."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/4\/1262\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:22:30Z","timestamp":1760160150000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/4\/1262"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,10]]},"references-count":43,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["s21041262"],"URL":"https:\/\/doi.org\/10.3390\/s21041262","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,10]]}}}