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Optimization of Deep Architectures for EEG Signal Classification: An AutoML Approach Using Evolutionary Algorithms

Aquino-Brítez, Diego and Ortiz, Andrés and Ortega, Julio and León, Javier and Formoso, Marco and Gan, John Q and Escobar, Juan José (2021) 'Optimization of Deep Architectures for EEG Signal Classification: An AutoML Approach Using Evolutionary Algorithms.' Sensors, 21 (6). p. 2096. ISSN 1424-8220

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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.

Item Type: Article
Uncontrolled Keywords: Electroencephalography; Artifacts; Algorithms; Signal Processing, Computer-Assisted; Brain-Computer Interfaces; Neural Networks, Computer
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 03 Aug 2021 13:54
Last Modified: 15 Jan 2022 01:37

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