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The major challenges involved in the task are extracting meaningful features from the signals and building an accurate model. This paper proposes a fuzzy ensemble-based deep learning approach to classify emotions from EEG-based models. Three individual deep learning models have been trained and combined using a fuzzy rank-based approach implemented using the Gompertz function. The model has been tested on two benchmark datasets: DEAP and AMIGOS. Our model has achieved 90.84% and 91.65% accuracies on the valence and arousal dimensions, respectively, for the DEAP dataset. The model also achieved accuracy above 95% on the DEAP dataset for the subject-dependent approach. On the AMIGOS dataset, our model has achieved state-of-the-art accuracies of 98.73% and 98.39% on the valence and arousal dimensions, respectively. The model achieved accuracies of 99.38% and 98.66% for the subject-independent and subject-dependent cases, respectively. The proposed model has provided satisfactory results on both DEAP and AMIGOS datasets and in both subject-dependent and subject-independent setups. Hence, we can conclude that this is a robust model for emotion recognition from EEG signals.\n<\/jats:p>","DOI":"10.1007\/s12559-023-10171-2","type":"journal-article","created":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T04:01:40Z","timestamp":1691553700000},"page":"1364-1378","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":63,"title":["A Fuzzy Ensemble-Based Deep learning Model for EEG-Based Emotion Recognition"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9325-9286","authenticated-orcid":false,"given":"Trishita","family":"Dhara","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9598-7981","authenticated-orcid":false,"given":"Pawan Kumar","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2037-8348","authenticated-orcid":false,"given":"Mufti","family":"Mahmud","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,8,9]]},"reference":[{"key":"10171_CR1","doi-asserted-by":"publisher","first-page":"163225","DOI":"10.1109\/ACCESS.2020.3016981","volume":"8","author":"L Farah","year":"2020","unstructured":"Farah L, Hussain A, Kerrouche A, Ieracitano C, Ahmad J, Mahmud M. 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