7 papers
Information-theoretic Multimodal Representation Learning for Electrocardiogram Signals
Phu X. Nguyen, Konstantinos Kontras, Wei Dai +5
Electrocardiograms (ECGs) are widely used non-invasive measurements of cardiac activity and play a central role in clinical diagnosis. Recent multimodal approaches align ECG signal…
NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfaces
Konstantinos Kontras, Trui Osselaer, Stylianos G. Mouslech +12
Foundation models (FMs) promise to extract unified representations that generalize across downstream tasks. They have emerged across fields, including electroencephalography (EEG),…
SynIB: Informational Bottleneck for Maximizing Synergy in Multimodal Learning
Konstantinos Kontras, Teodora Gagaleska, Thomas Strypsteen +4
A central objective in multimodal learning is to capture synergy: task-relevant information that arises only from the joint use of multiple modalities, and is not available from an…
ECG-Soup: Harnessing Multi-Layer Synergy for ECG Foundation Models
Phu X. Nguyen, Huy Phan, Hieu Pham +3
Transformer-based foundation models for Electrocardiograms (ECGs) have recently achieved impressive performance in many downstream applications.
Balancing Multimodal Training Through Game-Theoretic Regularization
Konstantinos Kontras, Thomas Strypsteen, Christos Chatzichristos +3
Multimodal learning holds promise for richer information extraction by capturing dependencies across data sources. Yet, current training methods often underperform due to modality…
SeizeIT2: Wearable Dataset Of Patients With Focal Epilepsy
Miguel Bhagubai, Christos Chatzichristos, Lauren Swinnen +10
The increasing technological advancements towards miniaturized physiological measuring devices have enabled continuous monitoring of epileptic patients outside of specialized envir…