LUNA: Efficient and Topology Agnostic Foundation Model for EEG LUNA (Latent Unified Network Architecture) is a self supervised foundation model for EEG that makes models agnostic to electrode topology . LUNA projects arbitrary channel layouts into a fixed size latent space with learned queries + cross attention , then runs patch wise temporal self attention only on this compact latent. This decouples compute from channel count , yielding linear in channels scaling , large FLOPs/memory savings, and strong transfer across datasets and montages. 🔒 License & Usage Policy (Weights) Weights license: The released model weights are licensed under Creative Commons Attribution–NoDerivatives 4.0 (CC BY ND 4.0) . This section summarizes the practical implications for users. This is not legal advice; please read the full license text. ✅ You may Use and redistribute the unmodified LUNA weights (including in commercial settings) with proper attribution to the LUNA authors. Fine tune / adapt the weights for your internal use (research or production) without redistributing the modified weights. Publish your code, configs, logs, and papers describing experiments with LUNA (please cite the paper). �…
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