Datasets:
Duplicate from Lance1573/L-Mind
Browse filesCo-authored-by: Lance <Lance1573@users.noreply.huggingface.co>
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train_speech.jsonl filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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license: mit
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pretty_name: L-Mind
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size_categories:
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- 10K<n<100K
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task_categories:
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- image-to-image
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language:
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- en
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- zh
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tags:
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- eeg
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- fnirs
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- bci
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- image-editing
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- multimodal
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configs:
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- config_name: speech
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data_files:
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- split: train
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path: train_speech.jsonl
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- split: test
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path: test_speech.jsonl
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- config_name: legacy
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data_files:
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- split: train
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path: train_0424.jsonl
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- split: test
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path: test_0424.jsonl
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---
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# L-Mind: A Multimodal Dataset for Neural-Driven Image Editing
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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.
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## 📄 Overview
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**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.
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- **Total Samples:** 23,928 image editing pairs
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- **Participants:** 12 subjects (plus cross-subject evaluation data)
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- **Task:** Instruction-based image editing viewed and conceived by users
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## 🧠 Modalities
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The dataset features synchronized recordings of:
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1. **EEG** (Electroencephalography): Captures rapid neural dynamics (4 channels: Pz, Fp2, Fpz, Oz).
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2. **fNIRS** (Functional Near-Infrared Spectroscopy): Measures hemodynamic responses (cognitive load/emotion).
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3. **PPG** (Photoplethysmography): Monitors physiological state (heart rate/stress).
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4. **Head Motion**: 6-axis IMU data tracking user movement.
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5. **Speech**: Audio instructions provided by users.
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6. **Visuals**: Source Image, Target Image, and Text Instruction.
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## 🚀 Applications
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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:
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- Background replacement
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- Object manipulation
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- Global stylistic changes
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- Text editing
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## 🔗 Resources
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- **Project Website:** [https://loongx1.github.io](https://loongx1.github.io)
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- **Paper:** [Neural-Driven Image Editing](https://arxiv.org/abs/2507.05397)
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## 📚 Citation
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If you use this dataset, please cite:
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```bibtex
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@inproceedings{zhouneural,
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title={Neural-Driven Image Editing},
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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},
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booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems}
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}
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```
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version https://git-lfs.github.com/spec/v1
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oid sha256:7d65eaea4d277bab0f5d8ae30521bbdb79daa7f07652e07739347fb3e7307416
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size 2087892569
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