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README.md
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# Install the required packages
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pip install -r requirements.txt
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### Step 2: Download the Model
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local_dir="./checkpoints" # Download to a local 'checkpoints' folder
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)
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print(f"Model downloaded to: {model_path}")
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### Step 3: Run Inference
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--ckpt ./checkpoints/unifiedneurogen-eeg2fmri-nat-view-within-subject-demo.pt \
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--eeg-path /path/to/your/test/eeg/encodings \
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--save-path /path/to/save/generated/data
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--ckpt: The path to the model checkpoint you downloaded in Step 2.
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--save-path: The directory where the generated fMRI data will be saved.
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📜 Citation
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If you use this model or the UnifiedNeuroGen framework in your research, please cite the original paper:
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```bash
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@misc{yao2025empoweringfunctionalneuroimagingpretrained,
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={[https://arxiv.org/abs/2506.02433](https://arxiv.org/abs/2506.02433)},
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}
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# Install the required packages
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pip install -r requirements.txt
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```
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### Step 2: Download the Model
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local_dir="./checkpoints" # Download to a local 'checkpoints' folder
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)
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print(f"Model downloaded to: {model_path}")
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```
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### Step 3: Run Inference
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--ckpt ./checkpoints/unifiedneurogen-eeg2fmri-nat-view-within-subject-demo.pt \
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--eeg-path /path/to/your/test/eeg/encodings \
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--save-path /path/to/save/generated/data
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```
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--ckpt: The path to the model checkpoint you downloaded in Step 2.
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--save-path: The directory where the generated fMRI data will be saved.
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## 📜 Citation
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If you use this model or the UnifiedNeuroGen framework in your research, please cite the original paper:
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```bash
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@misc{yao2025empoweringfunctionalneuroimagingpretrained,
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={[https://arxiv.org/abs/2506.02433](https://arxiv.org/abs/2506.02433)},
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}
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```
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