sensors Article Optimization of Deep Architectures for EEG Signal Classification: An AutoML Approach Using Evolutionary Algorithms Diego Aquino-Brítez 1, Andrés Ortiz 2,* , Julio Ortega 1, Javier León 1, Marco Formoso 2 , John Q. Gan 3 and Juan José Escobar 1 1 Department of Computer Architecture and Technology, University of Granada, 18014 Granada, Spain;
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[email protected] Abstract: 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 architec- Citation: Aquino-Brítez, D.; Ortiz, tures include a vast number of hyperparameters on which the performance of the model relies. In this A.; Ortega, J.; León, J.; Formoso, M.; paper, we propose a method for optimizing Deep Learning models, not only the hyperparameters, Gan, J.Q.; Escobar, J.J.