37 citations · 56 across the 10 of their papers we have counts for
5 papers · 1 filter
Riemannian Geometry-Preserving Variational Autoencoder for MI-BCI Data Augmentation
Viktorija Poļaka, Ivo Pascal de Jong, Andreea Ioana Sburlea
This paper addresses the challenge of generating synthetic electroencephalogram (EEG) covariance matrices for motor imagery brain-computer interface (MI-BCI) applications. Objectiv…
The Challenge of Out-Of-Distribution Detection in Motor Imagery BCIs
Merlijn Quincent Mulder, Matias Valdenegro-Toro, Andreea Ioana Sburlea +1
Machine Learning classifiers used in Brain-Computer Interfaces make classifications based on the distribution of data they were trained on. When they need to make inferences on sam…
Uncertainty Quantification for Motor Imagery BCI -- Machine Learning vs. Deep Learning
Joris Suurmeijer, Ivo Pascal de Jong, Matias Valdenegro-Toro +1
Brain-computer interfaces (BCIs) turn brain signals into functionally useful output, but they are not always accurate. A good Machine Learning classifier should be able to indicate…
Measuring Orthogonality as the Blind-Spot of Uncertainty Disentanglement
Ivo Pascal de Jong, Andreea Ioana Sburlea, Matthia Sabatelli +1
Aleatoric (data) and epistemic (knowledge) uncertainty are textbook components of Uncertainty Quantification. Jointly estimating these components has been shown to be problematic a…
Uncertainty Quantification for cross-subject Motor Imagery classification
Prithviraj Manivannan, Ivo Pascal de Jong, Matias Valdenegro-Toro +1
Uncertainty Quantification aims to determine when the prediction from a Machine Learning model is likely to be wrong. Computer Vision research has explored methods for determining…