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

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

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 dis...

Full description

Bibliographic Details
Main Authors: Diego Aquino-Brítez, Andrés Ortiz, Julio Ortega, Javier León, Marco Formoso, John Gan, Juan J. Escobar
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Sensors
Subjects:
brain-computer interfaces (BCI)
evolutionary computing
multi-objective EEG classification
deep learning
Online Access:https://www.mdpi.com/1424-8220/21/6/2096

Internet

https://www.mdpi.com/1424-8220/21/6/2096

Similar Items