{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T16:53:11Z","timestamp":1768409591200,"version":"3.49.0"},"reference-count":25,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,2,25]],"date-time":"2021-02-25T00:00:00Z","timestamp":1614211200000},"content-version":"vor","delay-in-days":55,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Electroencephalography (EEG) is the measurement of neuronal activity in different areas of the brain through the use of electrodes. As EEG signal technology has matured over the years, it has been applied in various methods to EEG emotion recognition, most significantly including the use of convolutional neural network (CNN). However, these methods are still not ideal, and shortcomings have been found in the results of some models of EEG feature extraction and classification. In this study, two CNN models were selected for the extraction and classification of preprocessed data, namely, common spatial patterns\u2010 (CSP\u2010) CNN and wavelet transform\u2010 (WT\u2010) CNN. Using the CSP\u2010CNN, we first used the common space model to reduce dimensionality and then applied the CNN directly to extract and classify the features of the EEG; while, with the WT\u2010CNN model, we used the wavelet transform to extract EEG features, thereafter applying the CNN for classification. The EEG classification results of these two classification models were subsequently analyzed and compared, with the average classification accuracy of the CSP\u2010CNN model found to be 80.56%, and the average classification accuracy of the WT\u2010CNN model measured to 86.90%. Thus, the findings of this study show that the average classification accuracy of the WT\u2010CNN model was 6.34% higher than that of the CSP\u2010CNN.<\/jats:p>","DOI":"10.1155\/2021\/6625141","type":"journal-article","created":{"date-parts":[[2021,2,25]],"date-time":"2021-02-25T18:50:05Z","timestamp":1614279005000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Analytical Comparison of Two Emotion Classification Models Based on Convolutional Neural Networks"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5087-7589","authenticated-orcid":false,"given":"Huiping","family":"Jiang","sequence":"first","affiliation":[]},{"given":"Demeng","family":"Wu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7516-0469","authenticated-orcid":false,"given":"Rui","family":"Jiao","sequence":"additional","affiliation":[]},{"given":"Zongnan","family":"Wang","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,2,25]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.14569\/ijacsa.2017.080955"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.12.038"},{"key":"e_1_2_8_3_2","unstructured":"SimKokS.andYouL. Z. Fast fourier analysis and EEG classification brainwave controlled wheelchair Proceedings Of 2016 2nd International Conference on Control Science and Systems Engineering (ICCSSE) pp. 20-23 July 2016 Singapore."},{"key":"e_1_2_8_4_2","doi-asserted-by":"crossref","unstructured":"WenZ. XuR. andDuJ. A novel convolutional neural networks for emotion recognition based on EEG signal Proceedings of the 2017 International Conference On Security Pattern Analysis And Cybernetics (Spac) pp. 672-677 December 2017 Guangzhou China.","DOI":"10.1109\/SPAC.2017.8304360"},{"key":"e_1_2_8_5_2","doi-asserted-by":"publisher","DOI":"10.14569\/ijacsa.2017.081046"},{"key":"e_1_2_8_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2018.08.114"},{"key":"e_1_2_8_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40846-016-0214-0"},{"key":"e_1_2_8_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59421-7_2"},{"key":"e_1_2_8_9_2","doi-asserted-by":"publisher","DOI":"10.3233\/BME-130919"},{"key":"e_1_2_8_10_2","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899x\/782\/3\/032005"},{"key":"e_1_2_8_11_2","unstructured":"WuN. Research on Emotion Classification Based on EEG Signal M. 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Classification of EEG signal by WT-CNN model in emotion recognition system Proceedings of the IEEE 16th International Conference on Cognitive Informatics and Cognitive Computing (ICCI\u2217CC) July 2017 New York NY USA.","DOI":"10.1109\/ICCI-CC.2017.8109738"},{"key":"e_1_2_8_13_2","doi-asserted-by":"publisher","DOI":"10.1364\/OL.43.001810"},{"key":"e_1_2_8_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.3003056"},{"key":"e_1_2_8_15_2","first-page":"342","article-title":"Seismic time-frequency analysis based on entropy-optimized Paul wavelet transform","volume":"17","author":"Yi L.","year":"2020","journal-title":"Institute of Electrical and Electronics Engineers Geoscience and Remote Sensing Letters"},{"key":"e_1_2_8_16_2","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.23730"},{"key":"e_1_2_8_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/taffc.2018.2817622"},{"key":"e_1_2_8_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2017.09.017"},{"key":"e_1_2_8_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2014.02.043"},{"key":"e_1_2_8_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/tnsre.2019.2905894"},{"key":"e_1_2_8_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.109991"},{"key":"e_1_2_8_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2018.02.020"},{"key":"e_1_2_8_23_2","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aace8c"},{"key":"e_1_2_8_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.09.071"},{"key":"e_1_2_8_25_2","first-page":"1724","article-title":"Multi-SLM color holographic 3D display based on RGB spatial filter","volume":"12","author":"Zaperty W.","year":"2016","journal-title":"Journal of Display Technology"}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2021\/6625141.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2021\/6625141.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/6625141","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T22:38:59Z","timestamp":1723243139000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/6625141"}},"subtitle":[],"editor":[{"given":"Ning","family":"Cai","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":25,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/6625141"],"URL":"https:\/\/doi.org\/10.1155\/2021\/6625141","archive":["Portico"],"relation":{},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"value":"1076-2787","type":"print"},{"value":"1099-0526","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2020-12-30","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-02-11","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-02-25","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"6625141"}}