--- a/README.md
+++ b/README.md
@@ -1,47 +1,46 @@
-# EEG-Conformer
-
-### EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization [[Paper](https://ieeexplore.ieee.org/document/9991178)]
-##### Core idea: spatial-temporal conv + pooling + self-attention
-
-### News
-#### 🎉🎉🎉 We've joined in [braindecode](https://braindecode.org/stable/index.html) toolbox. Use [**here**](https://braindecode.org/stable/generated/braindecode.models.EEGConformer.html) for detailed info.
-
-
-Thanks to [Bru](https://github.com/bruAristimunha) and colleagues for helping with the modifications.
-
-## Abstract
-![Network Architecture](/visualization/Fig1.png)
-
-- We propose a compact convolutional Transformer, EEG Conformer, to encapsulate local and global features in a unified EEG classification framework.  
-- The convolution module learns the low-level local features throughout the one-dimensional temporal and spatial convolution layers. The self-attention module is straightforwardly connected to extract the global correlation within the local temporal features. Subsequently, the simple classifier module based on fully-connected layers is followed to predict the categories for EEG signals. 
-- We also devise a visualization strategy to project the class activation mapping onto the brain topography.
-
-
-## Requirements:
-- Python 3.10
-- Pytorch 1.12
-
-
-## Datasets
-Please use consistent train-val-test split when comparing with other methods.
-- [BCI_competition_IV2a](https://www.bbci.de/competition/iv/) - acc 78.66% (hold out)
-- [BCI_competition_IV2b](https://www.bbci.de/competition/iv/) - acc 84.63% (hold out)
-- [SEED](https://bcmi.sjtu.edu.cn/home/seed/seed.html) - acc 95.30% (5-fold)
-
-
-## Citation
-Hope this code can be useful. I would appreciate you citing us in your paper. 😊
-```
-@article{song2023eeg,
-  title = {{{EEG Conformer}}: {{Convolutional Transformer}} for {{EEG Decoding}} and {{Visualization}}},
-  shorttitle = {{{EEG Conformer}}},
-  author = {Song, Yonghao and Zheng, Qingqing and Liu, Bingchuan and Gao, Xiaorong},
-  year = {2023},
-  journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
-  volume = {31},
-  pages = {710--719},
-  issn = {1558-0210},
-  doi = {10.1109/TNSRE.2022.3230250}
-}
-``` 
-
+# EEG-Conformer
+
+### EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization [[Paper](https://ieeexplore.ieee.org/document/9991178)]
+##### Core idea: spatial-temporal conv + pooling + self-attention
+
+### News
+#### 🎉🎉🎉 We've joined in [braindecode](https://braindecode.org/stable/index.html) toolbox. Use [**here**](https://braindecode.org/stable/generated/braindecode.models.EEGConformer.html) for detailed info.
+
+
+Thanks to [Bru](https://github.com/bruAristimunha) and colleagues for helping with the modifications.
+
+## Abstract
+
+- We propose a compact convolutional Transformer, EEG Conformer, to encapsulate local and global features in a unified EEG classification framework.  
+- The convolution module learns the low-level local features throughout the one-dimensional temporal and spatial convolution layers. The self-attention module is straightforwardly connected to extract the global correlation within the local temporal features. Subsequently, the simple classifier module based on fully-connected layers is followed to predict the categories for EEG signals. 
+- We also devise a visualization strategy to project the class activation mapping onto the brain topography.
+
+
+## Requirements:
+- Python 3.10
+- Pytorch 1.12
+
+
+## Datasets
+Please use consistent train-val-test split when comparing with other methods.
+- [BCI_competition_IV2a](https://www.bbci.de/competition/iv/) - acc 78.66% (hold out)
+- [BCI_competition_IV2b](https://www.bbci.de/competition/iv/) - acc 84.63% (hold out)
+- [SEED](https://bcmi.sjtu.edu.cn/home/seed/seed.html) - acc 95.30% (5-fold)
+
+
+## Citation
+Hope this code can be useful. I would appreciate you citing us in your paper. 😊
+```
+@article{song2023eeg,
+  title = {{{EEG Conformer}}: {{Convolutional Transformer}} for {{EEG Decoding}} and {{Visualization}}},
+  shorttitle = {{{EEG Conformer}}},
+  author = {Song, Yonghao and Zheng, Qingqing and Liu, Bingchuan and Gao, Xiaorong},
+  year = {2023},
+  journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
+  volume = {31},
+  pages = {710--719},
+  issn = {1558-0210},
+  doi = {10.1109/TNSRE.2022.3230250}
+}
+``` 
+