6 papers
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…
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…
NLP Methods May Actually Be Better Than Professors at Estimating Question Difficulty
Leonidas Zotos, Ivo Pascal de Jong, Matias Valdenegro-Toro +3
Estimating the difficulty of exam questions is essential for developing good exams, but professors are not always good at this task. We compare various Large Language Model-based m…
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…
Uncertainty Quantification in Machine Learning for Biosignal Applications -- A Review
Ivo Pascal de Jong, Andreea Ioana Sburlea, Matias Valdenegro-Toro
Uncertainty Quantification (UQ) has gained traction in an attempt to improve the interpretability and robustness of machine learning predictions. Specifically (medical) biosignals…