Datasets:
File size: 2,587 Bytes
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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}
}
```
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