Model card: link the arXiv paper, add BibTeX

README.md

@@ -12,8 +12,8 @@ tags:
 
 # eeg-fm-masking_mae_rone_L33
 
-Pretrained EEG encoder from the paper *What masking geometry works best for EEG foundation models? A controlled evaluation across MAE and JEPA*
-([website](https://pierregtch.github.io/eeg-fm-masking) · [code](https://github.com/PierreGtch/eeg-fm-masking) · [all 58 models](https://huggingface.co/collections/PierreGtch/eeg-fm-masking-6ab912b6a03bba1348fc7366)).
+Pretrained EEG encoder from the paper *What masking geometry works best for EEG foundation models?*
+([arXiv:2609.33487](https://arxiv.org/abs/2609.33487) · [website](https://pierregtch.github.io/eeg-fm-masking) · [code](https://github.com/PierreGtch/eeg-fm-masking) · [all 58 models](https://huggingface.co/collections/PierreGtch/eeg-fm-masking-6ab912b6a03bba1348fc7366)).
 
 It is one of **58 encoders trained under an identical recipe** where only the masking
 geometry changes: 5 spatial radii × 6 temporal lengths × 2 frameworks
@@ -135,4 +135,16 @@ Frozen encoder, ridge regression/classification on the flattened contextual feat
 ## License and citation
 
 Weights released under **CC-BY-4.0**; code under MIT ([GitHub](https://github.com/PierreGtch/eeg-fm-masking)).
-If you use these models, please cite the paper (reference on the [GitHub page](https://github.com/PierreGtch/eeg-fm-masking)).
+If you use these models, please cite the paper:
+
+```bibtex
+@misc{guetschel2026maskinggeometryworksbest,
+      title={What masking geometry works best for EEG foundation models?},
+      author={Pierre Guetschel and Bruno Aristimunha and Yassine El Ouahidi and Arnaud Delorme and Thomas Moreau and Michael Tangermann},
+      year={2026},
+      eprint={2609.33487},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2609.33487},
+}
+```