| --- |
| license: mit |
| pretty_name: L-Mind |
| size_categories: |
| - 10K<n<100K |
|
|
| task_categories: |
| - image-to-image |
|
|
| language: |
| - en |
| - zh |
|
|
| tags: |
| - eeg |
| - fnirs |
| - bci |
| - image-editing |
| - multimodal |
|
|
| configs: |
| - config_name: speech |
| data_files: |
| - split: train |
| path: train_speech.jsonl |
| - split: test |
| path: test_speech.jsonl |
|
|
| - config_name: legacy |
| data_files: |
| - split: train |
| path: train_0424.jsonl |
| - split: test |
| path: test_0424.jsonl |
| --- |
| |
| # L-Mind: A Multimodal Dataset for Neural-Driven Image Editing |
|
|
| This dataset is part of the **NeurIPS 2025** paper: **"Neural-Driven Image Editing"**, which introduces **LoongX**, a hands-free image editing approach driven by multimodal neurophysiological signals. |
|
|
| ## 📄 Overview |
| **L-Mind** is a large-scale multimodal dataset designed to bridge Brain-Computer Interfaces (BCIs) with generative AI. It enables research into accessible, intuitive image editing for individuals with limited motor control or language abilities. |
|
|
| - **Total Samples:** 23,928 image editing pairs |
| - **Participants:** 12 subjects (plus cross-subject evaluation data) |
| - **Task:** Instruction-based image editing viewed and conceived by users |
|
|
| ## 🧠 Modalities |
| The dataset features synchronized recordings of: |
| 1. **EEG** (Electroencephalography): Captures rapid neural dynamics (4 channels: Pz, Fp2, Fpz, Oz). |
| 2. **fNIRS** (Functional Near-Infrared Spectroscopy): Measures hemodynamic responses (cognitive load/emotion). |
| 3. **PPG** (Photoplethysmography): Monitors physiological state (heart rate/stress). |
| 4. **Head Motion**: 6-axis IMU data tracking user movement. |
| 5. **Speech**: Audio instructions provided by users. |
| 6. **Visuals**: Source Image, Target Image, and Text Instruction. |
|
|
| ## 🚀 Applications |
| This dataset supports the training of neural-driven generative models (like LoongX) that can interpret user intent directly from brain and physiological signals to perform: |
| - Background replacement |
| - Object manipulation |
| - Global stylistic changes |
| - Text editing |
|
|
| ## 🔗 Resources |
| - **Project Website:** [https://loongx1.github.io](https://loongx1.github.io) |
| - **Paper:** [Neural-Driven Image Editing](https://arxiv.org/abs/2507.05397) |
|
|
| ## 📚 Citation |
| If you use this dataset, please cite: |
| ```bibtex |
| @inproceedings{zhouneural, |
| title={Neural-Driven Image Editing}, |
| author={Zhou, Pengfei and Xia, Jie and Peng, Xiaopeng and Zhao, Wangbo and Ye, Zilong and Li, Zekai and Yang, Suorong and Pan, Jiadong and Chen, Yuanxiang and Wang, Ziqiao and others}, |
| booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems} |
| } |
| ``` |
|
|
