Optimization of Deep Architectures for EEG Signal Classification: An AutoML Approach Using Evolutionary Algorithms
Metadatos
Mostrar el registro completo del ítemAutor
Aquino Brítez, Diego; Ortiz, Andrés; Ortega Lopera, Julio; Escobar Pérez, Juan José; Formoso, Marco; Gan, John Q.; Escobar Pérez, Juan JoséEditorial
MDPI
Materia
Brain-computer interfaces (BCI) Evolutionary computing Multi-objective EEG classification Deep learning
Fecha
2021-03-17Referencia bibliográfica
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
Patrocinador
MINECO/FEDER under PGC2018-098813- B-C31 and PGC2018-098813-B-C32 projectsResumen
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.