6 citations · 13 across the 13 of their papers we have counts for
14 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.
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…
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…
Improving Multimodal Learning with Multi-Loss Gradient Modulation
Konstantinos Kontras, Christos Chatzichristos, Matthew Blaschko +1
Learning from multiple modalities, such as audio and video, offers opportunities for leveraging complementary information, enhancing robustness, and improving contextual understand…
Multimodal wearable EEG, EMG and accelerometry measurements improve the accuracy of tonic-clonic seizure detection in-hospital
Jingwei Zhang, Lauren Swinnen, Christos Chatzichristos +23
Objective: Most current wearable tonic-clonic seizure (TCS) detection systems are based on extra-cerebral signals, such as electromyography (EMG) or accelerometry (ACC). Although m…
CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities
Konstantinos Kontras, Christos Chatzichristos, Huy Phan +2
Sleep abnormalities can have severe health consequences. Automated sleep staging, i.e. labelling the sequence of sleep stages from the patient's physiological recordings, could sim…
Automatic artifact removal of resting-state fMRI with Deep Neural Networks
Christos Theodoropoulos, Christos Chatzichristos, Sabine Van Huffel
Functional Magnetic Resonance Imaging (fMRI) is a non-invasive technique for studying brain activity. During an fMRI session, the subject executes a set of tasks (task-related fMRI…
Early soft and flexible fusion of EEG and fMRI via tensor decompositions
Christos Chatzichristos, Eleftherios Kofidis, Lieven De Lathauwer +2
Data fusion refers to the joint analysis of multiple datasets which provide complementary views of the same task. In this preprint, the problem of jointly analyzing electroencephal…
Assisted Dictionary Learning for fMRI Data Analysis
Manuel Morante Moreno, Yannis Kopsinis, Eleftherios Kofidis +2
Extracting information from functional magnetic resonance (fMRI) images has been a major area of research for more than two decades. The goal of this work is to present a new metho…
Joint Channel Estimation / Data Detection in MIMO-FBMC/OQAM Systems - A Tensor-Based Approach
Eleftherios Kofidis, Christos Chatzichristos, Andre L. F. de Almeida
Filter bank-based multicarrier (FBMC) systems are currently being considered as a prevalent candidate for replacing the long established cyclic prefix (CP)-based orthogonal frequen…
Higher-Order Block Term Decomposition for Spatially Folded fMRI Data
Christos Chatzichristos, Eleftherios Kofidis, Giannis Kopsinis +1
The growing use of neuroimaging technologies generates a massive amount of biomedical data that exhibit high dimensionality. Tensor-based analysis of brain imaging data has been pr…