{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T03:49:32Z","timestamp":1768708172732,"version":"3.49.0"},"reference-count":54,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,17]],"date-time":"2021-03-17T00:00:00Z","timestamp":1615939200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100014440","name":"Ministerio de Ciencia, Innovaci\u00f3n y Universidades","doi-asserted-by":"publisher","award":["PGC2018-098813-B-C31"],"award-info":[{"award-number":["PGC2018-098813-B-C31"]}],"id":[{"id":"10.13039\/100014440","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014440","name":"Ministerio de Ciencia, Innovaci\u00f3n y Universidades","doi-asserted-by":"publisher","award":["PGC2018-098813-B-C32"],"award-info":[{"award-number":["PGC2018-098813-B-C32"]}],"id":[{"id":"10.13039\/100014440","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Electroencephalography (EEG) signal classification is a challenging task due to the low signal-to-noise ratio and the usual presence of artifacts from different sources. Different classification techniques, which are usually based on a predefined set of features extracted from the EEG band power distribution profile, have been previously proposed. However, the classification of EEG still remains a challenge, depending on the experimental conditions and the responses to be captured. In this context, the use of deep neural networks offers new opportunities to improve the classification performance without the use of a predefined set of features. Nevertheless, Deep Learning architectures include a vast number of hyperparameters on which the performance of the model relies. In this paper, we propose a method for optimizing Deep Learning models, not only the hyperparameters, but also their structure, which is able to propose solutions that consist of different architectures due to different layer combinations. The experimental results corroborate that deep architectures optimized by our method outperform the baseline approaches and result in computationally efficient models. Moreover, we demonstrate that optimized architectures improve the energy efficiency with respect to the baseline models.<\/jats:p>","DOI":"10.3390\/s21062096","type":"journal-article","created":{"date-parts":[[2021,3,17]],"date-time":"2021-03-17T05:43:26Z","timestamp":1615959806000},"page":"2096","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Optimization of Deep Architectures for EEG Signal Classification: An AutoML Approach Using Evolutionary Algorithms"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7506-7547","authenticated-orcid":false,"given":"Diego","family":"Aquino-Br\u00edtez","sequence":"first","affiliation":[{"name":"Department of Computer Architecture and Technology, University of Granada, 18014 Granada, Spain"}]},{"given":"Andr\u00e9s","family":"Ortiz","sequence":"additional","affiliation":[{"name":"Department of Communications Engineering, University of M\u00e1laga, 29071 M\u00e1laga, Spain"}]},{"given":"Julio","family":"Ortega","sequence":"additional","affiliation":[{"name":"Department of Computer Architecture and Technology, University of Granada, 18014 Granada, Spain"}]},{"given":"Javier","family":"Le\u00f3n","sequence":"additional","affiliation":[{"name":"Department of Computer Architecture and Technology, University of Granada, 18014 Granada, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8969-1941","authenticated-orcid":false,"given":"Marco","family":"Formoso","sequence":"additional","affiliation":[{"name":"Department of Communications Engineering, University of M\u00e1laga, 29071 M\u00e1laga, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1230-7643","authenticated-orcid":false,"given":"John Q.","family":"Gan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK"}]},{"given":"Juan Jos\u00e9","family":"Escobar","sequence":"additional","affiliation":[{"name":"Department of Computer Architecture and Technology, University of Granada, 18014 Granada, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,17]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Demystifying Parallel and Distributed Deep Learning: An in-depth Concurrency Analysis","volume":"52","author":"Hoefler","year":"2019","journal-title":"ACM Comput. 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