{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:32:43Z","timestamp":1760232763675,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T00:00:00Z","timestamp":1669680000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61971173","GK209907299001-008","202210336051"],"award-info":[{"award-number":["61971173","GK209907299001-008","202210336051"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds for the Provincial Universities of Zhejiang","award":["61971173","GK209907299001-008","202210336051"],"award-info":[{"award-number":["61971173","GK209907299001-008","202210336051"]}]},{"name":"National Innovation Training Program for College Students","award":["61971173","GK209907299001-008","202210336051"],"award-info":[{"award-number":["61971173","GK209907299001-008","202210336051"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>The electroencephalogram (EEG) can objectively reflect the emotional state of human beings, and has attracted much attention in the academic circles in recent years. However, due to its weak, non-stationary, and low signal-to-noise properties, it is inclined to cause noise in the collected EEG data. In addition, EEG features extracted from different frequency bands and channels usually exhibit different levels of emotional expression abilities in emotion recognition tasks. In this paper, we fully consider the characteristics of EEG and propose a new model RSRRW (retargeted semi-supervised regression with robust weights). The advantages of the new model can be listed as follows. (1) The probability weight is added to each sample so that it could help effectively search noisy samples in the dataset, and lower the effect of them at the same time. (2) The distance between samples from different categories is much wider than before by extending the \u03f5-dragging method to a semi-supervised paradigm. (3) Automatically discover the EEG emotional activation mode by adaptively measuring the contribution of sample features through feature weights. In the three cross-session emotion recognition tasks, the average accuracy of the RSRRW model is 81.51%, which can be seen in the experimental results on the SEED-IV dataset. In addition, with the support of the Friedman test and Nemenyi test, the classification of RSRRW model is much more accurate than that of other models.<\/jats:p>","DOI":"10.3390\/systems10060236","type":"journal-article","created":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T04:05:24Z","timestamp":1669781124000},"page":"236","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["EEG-Based Emotion Recognition by Retargeted Semi-Supervised Regression with Robust Weights"],"prefix":"10.3390","volume":"10","author":[{"given":"Ziyuan","family":"Chen","sequence":"first","affiliation":[{"name":"Zhuoyue Honors College, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5619-1166","authenticated-orcid":false,"given":"Shuzhe","family":"Duan","sequence":"additional","affiliation":[{"name":"Zhuoyue Honors College, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"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":"Zhejiang Key Laboratory of Brain-Machine Collaborative Intelligence, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Papero, D., Frost, R., Havstad, L., and Noone, R. 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