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...

Full description

Autores:

milanés hermosilla, daily
Trujillo Codorniú, Rafael
López-Baracaldo, René
Sagaro Zamora, Roberto
Delisle-Rodriguez, Denis
Villarejo Mayor, John Jairo
Núñez Alvarez, José Ricardo

Tipo de recurso:

Article of journal

Fecha de publicación:

2021

Institución:

Corporación Universidad de la Costa

Repositorio:

REDICUC - Repositorio CUC

Idioma:

eng

OAI Identifier:

oai:repositorio.cuc.edu.co:11323/8928

Acceso en línea:

https://hdl.handle.net/11323/8928
https://doi.org/10.3390/s21217241
https://repositorio.cuc.edu.co/

Palabra clave:

Brain–computer interfaces
Monte Carlo dropout
Motor imagery
Shallow convolutional neural network
Uncertainty estimation

Rights

openAccess

License

CC0 1.0 Universal