collaborators

12 papers

cs.LG2026

S-CEReBrO: Breaking the Memory Barrier in Continuous EEG Monitoring

Glenn Anta Bucagu, Thorir Mar Ingolfsson, Yawei Li +1

The paper introduces S-CEReBrO, a streaming Transformer architecture that uses a windowed alternating attention mechanism to keep memory usage constant during continuous EEG monito…

cs.LG2026

Quantizing Recursive Reasoning Models

Thorir Mar Ingolfsson, Wajeeha Tahir, Anna Tegon +3

Recursive reasoning models solve hard puzzles by applying compact, weight-tied blocks over many refinement steps. Because these blocks are reused many times, quantizing them create…

eess.SP2026

Evaluation of EEG Foundation Models for Event-Based Burst-Suppression Detection in ICU

Elisa Vasta, Thorir Mar Ingolfsson, Andrea Cossettini +4

Burst suppression (BS) is a clinically relevant electroencephalographic (EEG) pattern used to monitor sedation depth and brain activity in critically ill patients, particularly dur…

cs.AI2026

LuMamba: Latent Unified Mamba for Electrode Topology-Invariant and Efficient EEG Modeling

Danaé Broustail, Anna Tegon, Thorir Mar Ingolfsson +2

Electroencephalography (EEG) enables non-invasive monitoring of brain activity across clinical and neurotechnology applications, yet building foundation models for EEG remains chal…

cs.AI2026

PanLUNA: An Efficient and Robust Query-Unified Multimodal Model for Edge Biosignal Intelligence

Marija Zelic, Anna Tegon, Yawei Li +2

Physiological foundation models (FMs) have shown promise for biosignal representation learning, yet most remain confined to a single modality such as EEG, ECG, or PPG, largely beca…

eess.SP2026

FEMBA on the Edge: Physiologically-Aware Pre-Training, Quantization, and Deployment of a Bidirectional Mamba EEG Foundation Model on an Ultra-low Power Microcontroller

Anna Tegon, Nicholas Lehmann, Yawei Li +3

Objective: To enable continuous, long-term neuro-monitoring on wearable devices by overcoming the computational bottlenecks of Transformer-based Electroencephalography (EEG) founda…