Self-Supervised Learning

Lightning talk on LuMamba — using LeJEPA “world-model” self-supervision to pretrain a tiny, montage-agnostic foundation model for biosignals.

Topology-invariant, linear-complexity EEG foundation model; first systematic study of LeJEPA for biosignals.

Poster presentation of LUNA—our topology-agnostic EEG foundation model—at NeurIPS 2025 in San Diego.

Topology-agnostic EEG foundation model that delivers state-of-the-art performance with 300× fewer FLOPs and 10× lower memory usage.

Bidirectional Mamba EEG foundation model that matches transformer accuracy while scaling linearly with sequence length.

Alternating-attention EEG foundation model pre-trained on 20k+ hours that doubles speed and cuts memory by 6× versus standard transformers.