{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T10:28:40Z","timestamp":1784543320543,"version":"3.55.0"},"reference-count":51,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T00:00:00Z","timestamp":1656547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["51805332"],"award-info":[{"award-number":["51805332"]}]},{"name":"National Natural Science Foundation of China","award":["52072320"],"award-info":[{"award-number":["52072320"]}]},{"name":"National Natural Science Foundation of China","award":["JCYJ20190808142613246"],"award-info":[{"award-number":["JCYJ20190808142613246"]}]},{"DOI":"10.13039\/501100012271","name":"Shenzhen Fundamental Research Fund","doi-asserted-by":"publisher","award":["51805332"],"award-info":[{"award-number":["51805332"]}],"id":[{"id":"10.13039\/501100012271","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012271","name":"Shenzhen Fundamental Research Fund","doi-asserted-by":"publisher","award":["52072320"],"award-info":[{"award-number":["52072320"]}],"id":[{"id":"10.13039\/501100012271","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012271","name":"Shenzhen Fundamental Research Fund","doi-asserted-by":"publisher","award":["JCYJ20190808142613246"],"award-info":[{"award-number":["JCYJ20190808142613246"]}],"id":[{"id":"10.13039\/501100012271","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Various lengths of time window have been used in feature extraction for electroencephalogram (EEG) signal processing in previous studies. However, the effect of time window length on feature extraction for the downstream tasks such as emotion recognition has not been well examined. To this end, we investigate the effect of different time window (TW) lengths on human emotion recognition to find the optimal TW length for extracting electroencephalogram (EEG) emotion signals. Both power spectral density (PSD) features and differential entropy (DE) features are used to evaluate the effectiveness of different TW lengths based on the SJTU emotion EEG dataset (SEED). Different lengths of TW are then processed with an EEG feature-processing approach, namely experiment-level batch normalization (ELBN). The processed features are used to perform emotion recognition tasks in the six classifiers, the results of which are then compared with the results without ELBN. The recognition accuracies indicate that a 2-s TW length has the best performance on emotion recognition and is the most suitable to be used in EEG feature extraction for emotion recognition. The deployment of ELBN in the 2-s TW can further improve the emotion recognition performances by 21.63% and 5.04% when using an SVM based on PSD and DE features, respectively. These results provide a solid reference for the selection of TW length in analyzing EEG signals for applications in intelligent systems.<\/jats:p>","DOI":"10.3390\/s22134939","type":"journal-article","created":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T01:40:36Z","timestamp":1656639636000},"page":"4939","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["The Effect of Time Window Length on EEG-Based Emotion Recognition"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3137-6089","authenticated-orcid":false,"given":"Delin","family":"Ouyang","sequence":"first","affiliation":[{"name":"Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yufei","family":"Yuan","sequence":"additional","affiliation":[{"name":"Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7889-4695","authenticated-orcid":false,"given":"Guofa","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zizheng","family":"Guo","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory of Integrated Transportation Big Data Application Technology, National United Engineering Laboratory of Integrated and Intelligent Transportation, and Comprehensive Transportation Key Laboratory of Sichuan Province, School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1038\/s42256-020-00280-0","article-title":"Estimation of continuous valence and arousal levels from faces in naturalistic conditions","volume":"3","author":"Toisoul","year":"2021","journal-title":"Nat. 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