Monte Carlo Dropout for Uncertainty Estimation and Motor Imagery Classification
Motor Imagery (MI)-based Brain–Computer Interfaces (BCIs) have been widely used as an alternative communication channel to patients with severe motor disabilities, achieving high classification accuracy through machine learning techniques. Recently, deep learning techniques have spotlighted the stat...
Guardado en:
Autores principales: | Daily Milanés-Hermosilla, Rafael Trujillo Codorniú, René López-Baracaldo, Roberto Sagaró-Zamora, Denis Delisle-Rodriguez, John Jairo Villarejo-Mayor, José Ricardo Núñez-Álvarez |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
MDPI AG
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/b665f6d72ba44165a4a48b85e53a98d8 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Application of the Monte Carlo method to estimate the uncertainty in the compressive strength test of high-strength concrete modelled with a multilayer perceptron
por: Moromi-Nakata,Isabel, et al.
Publicado: (2018) -
The Academic Dropout Wheel Analyzing the Antecedents of Higher Education Dropout in Education Studies
por: Hind Naaman
Publicado: (2021) -
Parameter uncertainty methods in evaluating a lumped hydrological model
por: Diaz-Ramirez,Jairo, et al.
Publicado: (2012) -
Monte Carlo methods and applications
Publicado: (1995) -
Evaluation of Project Duration Uncertainty using the Dependency Structure Matrix and Monte Carlo Simulations
por: Gálvez,Edelmira Delfina, et al.
Publicado: (2015)