1 citations · 2 across the 7 of their papers we have counts for
7 papers
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…
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…
Conditional Gumbel-Softmax for constrained feature selection with application to node selection in wireless sensor networks
Thomas Strypsteen, Alexander Bertrand
In this paper, we introduce Conditional Gumbel-Softmax as a method to perform end-to-end learning of the optimal feature subset for a given task and deep neural network (DNN) model…
A distributed neural network architecture for dynamic sensor selection with application to bandwidth-constrained body-sensor networks
Thomas Strypsteen, Alexander Bertrand
We propose a dynamic sensor selection approach for deep neural networks (DNNs), which is able to derive an optimal sensor subset selection for each specific input sample instead of…
Bandwidth-efficient distributed neural network architectures with application to body sensor networks
Thomas Strypsteen, Alexander Bertrand
In this paper, we describe a conceptual design methodology to design distributed neural network architectures that can perform efficient inference within sensor networks with commu…
End-to-end learnable EEG channel selection for deep neural networks with Gumbel-softmax
Thomas Strypsteen, Alexander Bertrand
Many electroencephalography (EEG) applications rely on channel selection methods to remove the least informative channels, e.g., to reduce the amount of electrodes to be mounted, t…