Add library and pipeline tag, include paper and Github info

README.md

@@ -1,4 +1,111 @@
 ---
 license: apache-2.0
+library_name: pytorch
+pipeline_tag: other
 ---
-### There is the pretrained weights of CBraMod.
+
+### There is the pretrained weights of CBraMod.
+
+# CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding
+
+[![Paper](https://img.shields.io/badge/arXiv-2412.07236-red)](https://arxiv.org/abs/2412.07236)
+[![Paper](https://img.shields.io/badge/Paper-ICLR-008B8B)](https://openreview.net/forum?id=NPNUHgHF2w)
+[![huggingface](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-FFD21E)](https://huggingface.co/weighting666/CBraMod)
+![GitHub Repo stars](https://img.shields.io/github/stars/wjq-learning/CBraMod)
+
+<div align="center">
+<img src="figure/CBraMod_logo.png" style="width: 15%;" />
+</div>
+
+<p align="center">
+    🔍&nbsp;<a href="#-about">About</a>
+    | 🔨&nbsp;<a href="#-setup">Setup</a>
+    | 🚢&nbsp;<a href="#-how-to-pretrain">How to Pretrain</a>
+    | ⛵&nbsp;<a href="#-how-to-finetune">How to Finetune</a>
+    | 🚀&nbsp;<a href="#-quick-start">Quick Start</a>
+    | 🔗&nbsp;<a href="#-citation">Citation</a>
+</p>
+
+🔥 NEWS: The paper "_CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding_" has been accepted by ICLR 2025!
+
+## 🔍 About
+We propose **CBraMod**, a novel EEG foundation model, for EEG decoding on various clinical and BCI application.
+The preprint version of our paper is available at [arXiv](https://arxiv.org/abs/2412.07236). 
+The camera-ready version of the paper will be available at [OpenReview](https://openreview.net/forum?id=NPNUHgHF2w).
+<div align="center">
+<img src="figure/model.png" style="width:100%;" />
+</div>
+
+## 🔨 Setup
+Install [Python](https://www.python.org/downloads/).
+
+Install [PyTorch](https://pytorch.org/get-started/locally/).
+
+Install other requirements:
+```commandline
+pip install -r requirements.txt
+``` 
+
+## 🚢 How to Pretrain
+You can pretrain CBraMod on our pretraining dataset or your custom pretraining dataset using the following code:
+```commandline
+python pretrain_main.py
+```
+We have released a pretrained checkpoint on [Hugginface🤗](https://huggingface.co/weighting666/CBraMod).
+
+## ⛵ How to Finetune
+You can finetune CBraMod on our selected downstream datasets using the following code:
+```commandline
+python finetune_main.py
+```
+
+## 🚀 Quick Start
+You can fine-tune the pretrained CBraMod on your custom downstream dataset using the following example code:
+```python
+import torch
+import torch.nn as nn
+from models.cbramod import CBraMod
+from einops.layers.torch import Rearrange
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+model = CBraMod().to(device)
+model.load_state_dict(torch.load('pretrained_weights/pretrained_weights.pth', map_location=device))
+model.proj_out = nn.Identity()
+classifier = nn.Sequential(
+  Rearrange('b c s p -> b (c s p)'),
+  nn.Linear(22*4*200, 4*200),
+  nn.ELU(),
+  nn.Dropout(0.1),
+  nn.Linear(4 * 200, 200),
+  nn.ELU(),
+  nn.Dropout(0.1),
+  nn.Linear(200, 4),
+).to(device)
+
+# mock_eeg.shape = (batch_size, num_of_channels, time_segments, points_per_patch)
+mock_eeg = torch.randn((8, 22, 4, 200)).to(device)
+
+# logits.shape = (batch_size, num_of_classes)
+logits = classifier(model(mock_eeg))
+```
+
+## 🔗 Citation
+If you're using this repository in your research or applications, please cite using the following BibTeX:
+```bibtex
+@inproceedings{wang2025cbramod,
+    title={{CB}raMod: A Criss-Cross Brain Foundation Model for {EEG} Decoding},
+    author={Jiquan Wang and Sha Zhao and Zhiling Luo and Yangxuan Zhou and Haiteng Jiang and Shijian Li and Tao Li and Gang Pan},
+    booktitle={The Thirteenth International Conference on Learning Representations},
+    year={2025},
+    url={https://openreview.net/forum?id=NPNUHgHF2w}
+}
+```
+
+## ⭐ Star History
+<div align="center">
+    <a href="https://star-history.com/#wjq-learning/CBraMod&Date">
+        <img src="https://api.star-history.com/svg?repos=wjq-learning/CBraMod&type=Date" style="width: 80%;" />
+    </a>
+</div>
+
+Code: https://github.com/wjq-learning/CBraMod