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...

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Bibliographic Details
Published in:Sensors
Main Authors: Daily Milanés-Hermosilla, Rafael Trujillo Codorniú, René López-Baracaldo, Roberto Sagaró-Zamora, Denis Delisle-Rodriguez, John Jairo Villarejo-Mayor, José Ricardo Núñez-Álvarez
Format: Article
Language:English
Published: MDPI AG 2021-10-01
Subjects:
Brain–Computer Interfaces
Monte Carlo dropout
motor imagery
Shallow Convolutional Neural Network
uncertainty estimation
Online Access:https://www.mdpi.com/1424-8220/21/21/7241

Internet

https://www.mdpi.com/1424-8220/21/21/7241

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