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LICENSE

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+Brain-OF Large checkpoint license
+
+The model checkpoint weights released in this repository are licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).
+
+Full license terms:
+https://creativecommons.org/licenses/by-nc/4.0/legalcode.en
+
+This notice applies to the checkpoint weights. The training and model implementation code is distributed separately under Apache License 2.0:
+https://github.com/HanningGuo/BrainOF/blob/main/LICENSE
+
+Source datasets retain their original licenses and access requirements.

README.md

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 ---
 license: cc-by-nc-4.0
+tags:
+  - pytorch
+  - brain-of
+  - neuroimaging
+  - fmri
+  - eeg
+  - meg
+  - omnifunctional modality
+  - brain foundation model
+  - joint pretraining
+  - arxiv:2602.23410
 ---
+
+<p align="center">
+  <img src="assets/brain-of-logo.png" width="80" alt="Brain-OF logo">
+</p>
+
+<h1 align="center">Brain-OF Large</h1>
+
+<p align="center">
+  <strong>Brain-OF: An Omnifunctional Foundation Model for fMRI, EEG and MEG</strong><br>
+  NeurIPS 2026
+</p>
+
+<p align="center">
+  <a href="https://hanningguo.github.io/BrainOF/"><img src="https://img.shields.io/badge/Project-Website-3564C0" alt="Project website"></a>
+  <a href="https://arxiv.org/abs/2602.23410"><img src="https://img.shields.io/badge/arXiv-2602.23410-B31B1B" alt="Paper on arXiv"></a>
+  <a href="https://github.com/HanningGuo/BrainOF"><img src="https://img.shields.io/badge/GitHub-Code-181717" alt="Code on GitHub"></a>
+</p>
+
+<!-- Replace YOUR_NAMESPACE throughout this card before publishing. -->
+
+## Introduction
+
+<p align="justify">
+Brain-OF Large is a foundation model jointly pretrained on <strong>fMRI, EEG, and MEG</strong>, aiming to learn richer representations through cross-modal synergy.
+<strong>ARNESS</strong> maps signals with varying channel/ROI counts and temporal resolutions to a shared latent space. <strong>Sparse MoE</strong> captures shared and modality-specific patterns, while <strong>DINT attention</strong> filters irrelevant context and integrates local and global dependencies. <strong>Masked Temporal-Frequency Modeling</strong> pretrains the model to reconstruct masked signals in the time and frequency domains.
+Pretrained on <strong>37 public datasets</strong> with <strong>5.87 million samples</strong> from <strong>32,278 participants</strong>, the model supports unimodal and paired multimodal downstream tasks.
+</p>
+
+## Model specifications
+
+| Property | Value |
+| --- | --- |
+| Total parameters | 331M |
+| Active parameters per token | 150M |
+| Backbone layers | 24 |
+| Patch size | 64 time points |
+| Checkpoint format | PyTorch `.pth`; DDP loading path |
+
+## Usage
+
+After [installing the environment](https://github.com/HanningGuo/BrainOF#installation), replace the checkpoint path below and run the example from the BrainOF repository root. It extracts a **512-dimensional embedding** from a randomly generated signal:
+
+```python
+import numpy as np
+import torch
+from datasets import DataTransform
+from models.transformer import brainof_large_331M_A150M
+
+model = brainof_large_331M_A150M(num_classes=0, layer_scale_type="per_channel")
+checkpoint = torch.load(
+    "/path/to/brainof_large_331M_A150M.pth", map_location="cpu", weights_only=False
+)
+weights = {
+    k.removeprefix("encoder."): v
+    for k, v in checkpoint["model"].items() if k.startswith("encoder.")
+}
+model.load_state_dict(weights)
+model.eval()
+
+# Generate a random signal: 19 channels, 2,000 time points
+signal = np.random.default_rng(0).standard_normal((19, 2000)).astype(np.float32)
+transform = DataTransform(signal_size=signal.shape, patch_size=64, max_seq_length=2048)
+patches, mask, _ = transform(signal)
+x = torch.tensor(patches, dtype=torch.float32).unsqueeze(0)
+attn_mask = torch.tensor(mask, dtype=torch.bool).unsqueeze(0)
+
+with torch.inference_mode():
+    embedding = model(x, attn_mask)
+print(embedding.shape)  # torch.Size([1, 512])
+```
+
+For real data, replace the random signal with a [preprocessed](https://github.com/HanningGuo/BrainOF#data-preparation) NumPy array of shape `(channels_or_ROIs, time_points)`. `DataTransform` normalizes, patches, and pads it. We recommend using at most **2,048 patch tokens** per sample and the token count must not exceed `max_seq_length` (see [Input Size and Tokenization](https://github.com/HanningGuo/BrainOF#input-size-and-tokenization)).
+
+## Model family
+
+| Model | Total parameters | Active / token | Layers | Embedding dim. |
+| :--- | ---: | ---: | ---: | ---: |
+| [![Brain-OF Base](https://img.shields.io/badge/Brain--OF-Base-FFE28A)](https://huggingface.co/han-ning/Brain-OF-Base) | 47.5M | 21.5M | 12 | 256 |
+| <a href="https://huggingface.co/han-ning/Brain-OF-Large" title="Current model"><img src="https://img.shields.io/badge/Brain--OF-Large-FFD21E" alt="Brain-OF Large — current model" style="outline: 2px solid #A87900; outline-offset: -2px; border-radius: 3px;"></a> | **331M** | **150M** | **24** | **512** |
+| [![Brain-OF Huge](https://img.shields.io/badge/Brain--OF-Huge-F4B400)](https://huggingface.co/han-ning/Brain-OF-Huge) | 1.7B | 500M | 36 | 768 |
+| [![Brain-OF Giant](https://img.shields.io/badge/Brain--OF-Giant-D99A00)](https://huggingface.co/han-ning/Brain-OF-Giant) | 9B | 1.2B | 48 | 1024 |
+
+## License
+
+This repository and the model weights are licensed under the [CC BY-NC 4.0](LICENSE).
+
+## Citation
+
+```bibtex
+@inproceedings{guo2026brainof,
+  title     = {{Brain-OF}: An Omnifunctional Foundation Model for {fMRI}, {EEG} and {MEG}},
+  author    = {Guo, Hanning and Bi, Hanwen and Abdellatif, Farah and Galbenus, Andrei and Shah, Jon. N. and Morrison, Abigail and Dammers, Jürgen},
+  booktitle = {Advances in Neural Information Processing Systems},
+  year      = {2026},
+  url       = {https://arxiv.org/abs/2602.23410}
+}
+```