Ivo Pascal de Jong

Abstract: Uncertainty Quantification (UQ) has gained traction in an attempt to improve the interpretability and robustness of machine learning predictions. Specifically (medical) biosignals such as electroencephalography (EEG), electrocardiography (ECG), electrooculography (EOG), and electromyography (EMG) could benefit from good UQ, since these suffer from a poor signal-to-noise ratio, and good human interpretability is pivotal for medical applications. To determine how uncertainty estimation can be used for biosignal tasks, we investigate current methods, use cases, applications, evaluations, and uncertainty measures. In this paper, we systematically review the state of the art of applying Uncertainty Quantification to Machine Learning tasks in the biosignal domain. All works from Web of Science, Scopus, IEEE XPlore and PsycINFO that discuss uncertainty in Machine Learning on one of the aforementioned biosignals is included. We present various methods, shortcomings, uncertainty measures and theoretical frameworks that currently exist in this application domain based on the 53 reviewed papers and related literature. We address misconceptions in the field, provide recommendations for future work, and discuss gaps in the literature in relation to diagnostic implementations as well as control for prostheses or brain-computer interfaces. Overall it can be concluded that promising UQ methods are available, but that research is needed on how people and systems may interact with an uncertainty-model in a (clinical) environment. Open Access Link

2026

Why Large Language Models can Secretly Outperform Embedding Similarity in Information Retrieval

64th Annual Meeting of the Association for Computational Linguistics (ACL) Student Research Workshop. Matei Benescu, Ivo Pascal De Jong

Abstract: With the emergence of Large Language Models (LLMs), new methods in Information Retrieval are available in which relevance is estimated directly through language understanding and reasoning, instead of embedding similarity. We argue that similarity is a short-sighted interpretation of relevance, and that LLM-Based Relevance Judgment Systems (LLM-RJS)(with reasoning) have potential to outperform Neural Embedding Retrieval Systems (NERS) by overcoming this limitation. Using the TREC-DL 2019 passage retrieval dataset, we compare various LLM-RJS with NERS, but observe no noticeable improvement. Subsequently, we analyze the impact of reasoning by comparing LLM-RJS with and without reasoning. We find that human annotations also suffer from short-sightedness, and that false-positives in the reasoning LLM-RJS are primarily mistakes in annotations due to short-sightedness. We conclude that LLM-RJS do have the ability to address the short-sightedness limitation in NERS, but that this cannot be evaluated with standard annotated relevance datasets. Open Access Link

2026

Riemannian Geometry-Preserving Variational Autoencoder for MI-BCI Data Augmentation

10th Graz BCI Conference. Viktorija Poļaka, Ivo Pascal de Jong, Andreea Ioana Sburlea

Abstract: This paper addresses the challenge of generating synthetic electroencephalogram (EEG) covariance matrices for motor imagery brain-computer interface (MI-BCI) applications. Objective: We aim to develop a generative model capable of producing high-fidelity synthetic covariance matrices while preserving their symmetric positive-definite nature. Approach: We propose a Riemannian geometry-preserving variational autoencoder (RGP-VAE) integrating geometric mappings with a composite loss function combining Riemannian distance, tangent space reconstruction accuracy and generative diversity. Results: The model generates valid, representative EEG covariance matrices, while learning a subject-invariant latent space. Synthetic data proves practically useful for MI-BCI, with its impact depending on the paired classifier. Contribution: This work introduces and validates the RGP-VAE as a geometry-preserving generative model for EEG covariance matrices, highlighting its potential for signal privacy, scalability and data augmentation. Open Access Link

2026

The Challenge of Out-Of-Distribution Detection in Motor Imagery BCIs

arXiv preprint. Merlijn Quincent Mulder, Matias Valdenegro-Toro, Andreea Ioana Sburlea, Ivo Pascal de Jong

Abstract: 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 samples that fall outside of this distribution, they can only make blind guesses. Instead of allowing random guesses, these Out-of-Distribution (OOD) samples should be detected and rejected. We study OOD detection in Motor Imagery BCIs by training a model on some classes and observing whether unfamiliar classes can be detected based on increased uncertainty. We test seven different OOD detection techniques and one more method that has been claimed to boost the quality of OOD detection. Our findings show that OOD detection for Brain-Computer Interfaces is more challenging than in other machine learning domains due to the high uncertainty inherent in classifying EEG signals. For many subjects, uncertainty for in-distribution classes can still be higher than for out-of-distribution classes. As a result, many OOD detection methods prove to be ineffective, though MC Dropout performed best. Additionally, we show that high in-distribution classification performance predicts high OOD detection performance, suggesting that improved accuracy can also lead to improved robustness. Our research demonstrates a setup for studying how models deal with unfamiliar EEG data and evaluates methods that are robust to these unfamiliar inputs. OOD detection can improve the overall safety and reliability of BCIs. Open Access Link

And 8 more
View all

Research

I'm interested in how Machine Learning methods behave when subjected to the reality of datasets that are often noisy, sparse or otherwise non-ideal. The research I do on Uncertainty in Machine Learning is therefore largely focused on empirical experiments where data's and algorithms interact. I'm specifically interested in distinguishing between data uncertainty and model uncertainty in Deep Learning.
For Brain-Computer Interfaces I'm interested in applying these Uncertain Machine Learning methods and seeing whether they give meaningful benefits to researcher or users. Personally, I'm most interested in Brain-Computer Interfaces when they can be useful for ALS, spinal cord injury, or stroke patients. Through decoding brain signals from movement attempts, I aim to achieve meaningful and useable control of a device.
I've selected some highlighted papers on the left. All publications are freely available as Open Access, just click the link.

Collaboration & Supervision

Interesting research comes from sharing interesting ideas, and many of my papers come from supervising excellent students or collaborating with excellent researchers. I am currently open to collaborations with:

Bachelor or Master students looking to do a research project.
Businesses interested in internship projects or with an interesting research problem.
Researchers looking to work with (uncertain) Machine Learning or Brain-Computer Interfaces.
Educators interested in AI.

If you're considering working with me, feel free to get in touch. I'd be happy to have a cup of coffee with you!

Get in touch!

Lecturer & PhD student

Research

Collaboration & Supervision

Teaching

Publicity & Outreach