37 citations · 56 across the 10 of their papers we have counts for
12 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…
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…
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…
Transferring BCI models from calibration to control: Observing shifts in EEG features
Ivo Pascal de Jong, Lüke Luna van den Wittenboer, Matias Valdenegro-Toro +1
Public Motor Imagery-based brain-computer interface (BCI) datasets are being used to develop increasingly good classifiers. However, they usually follow discrete paradigms where pa…
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…
Uncertainty Quantification in Machine Learning for Biosignal Applications -- A Review
Ivo Pascal de Jong, Andreea Ioana Sburlea, Matias Valdenegro-Toro
Purpose: Uncertainty Quantification (UQ) has gained traction in an attempt to improve the interpretability and robustness of machine learning predictions. Specifically (medical) bi…
Online asynchronous detection of error-related potentials in participants with a spinal cord injury using a generic classifier
Catarina Lopes-Dias, Andreea I. Sburlea, Katharina Breitegger +4
A BCI user awareness of an error is associated with a cortical signature named error-related potential (ErrP). The incorporation of ErrPs' detection in BCIs can improve BCIs' perfo…
HEAR to remove pops and drifts: the high-variance electrode artifact removal (HEAR) algorithm
Reinmar J. Kobler, Andreea I. Sburlea, Valeria Mondini +1
A high fraction of artifact-free signals is highly desirable in functional neuroimaging and brain-computer interfacing (BCI). We present the high-variance electrode artifact remova…
Advantages of EEG phase patterns for the detection of gait intention in healthy and stroke subjects
Andreea Ioana Sburlea, Luis Montesano, Javier Minguez
One use of EEG-based brain-computer interfaces (BCIs) in rehabilitation is the detection of movement intention. In this paper we investigate for the first time the instantaneous ph…
A guide to time-resolved and parameter-free measures of spike train synchrony
Mario Mulansky, Nebojsa Bozanic, Andreea Sburlea +1
Measures of spike train synchrony have proven a valuable tool in both experimental and computational neuroscience. Particularly useful are time-resolved methods such as the ISI- an…