Optimization of Deep Architectures for EEG Signal Classification: An AutoML Approach Using Evolutionary Algorithms Aquino Brítez, Diego Ortiz, Andrés Ortega Lopera, Julio Escobar Pérez, Juan José Formoso, Marco Gan, John Q. Escobar Pérez, Juan José Brain-computer interfaces (BCI) Evolutionary computing Multi-objective EEG classification Deep learning 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. 2021-05-26T11:23:43Z 2021-05-26T11:23:43Z 2021-03-17 info:eu-repo/semantics/article Aquino-Brítez, D.; Ortiz, A.; Ortega, J.; León, J.; Formoso, M.; Gan, J.Q.; Escobar, J.J. Optimization of Deep Architectures for EEG Signal Classification: An AutoML Approach Using Evolutionary Algorithms. Sensors 2021, 21, 2096. https:// doi.org/10.3390/s21062096 http://hdl.handle.net/10481/68749 10.3390/s21062096 eng http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess Atribución 3.0 España MDPI