self-supervised learning

LUNA: Efficient and Topology-Agnostic Foundation Model for EEG Signal Analysis
Topology-agnostic EEG foundation model that delivers state-of-the-art performance with 300× fewer FLOPs and 10× lower memory usage.
Berkay Döner, Thorir Mar Ingolfsson, Luca Benini, Yawei Li
PDF Code Project Preprint (arXiv) GitHub Repository DOI
LUNA: Efficient and Topology-Agnostic Foundation Model for EEG Signal Analysis
FEMBA: Efficient and Scalable EEG Analysis with a Bidirectional Mamba Foundation Model
Bidirectional Mamba EEG foundation model that matches transformer accuracy while scaling linearly with sequence length.
Anna Tegon, Thorir Mar Ingolfsson, Xiaying Wang, Luca Benini, Yawei Li
PDF Code Project Preprint (arXiv) GitHub Repository DOI
FEMBA: Efficient and Scalable EEG Analysis with a Bidirectional Mamba Foundation Model
CEReBrO: Compact Encoder for Representations of Brain Oscillations Using Efficient Alternating Attention
Alternating-attention EEG foundation model pre-trained on 20k+ hours that doubles speed and cuts memory by 6× versus standard transformers.
Alexandru Dimofte, Glenn Anta Bucagu, Thorir Mar Ingolfsson, Xiaying Wang, Andrea Cossettini, Luca Benini, Yawei Li
PDF Project Preprint (arXiv) DOI