| --- |
| license: cc-by-nc-4.0 |
| language: |
| - en |
| - ko |
| pretty_name: Revisiting Your Memory (EEG-AffectiveMemory dataset) |
| size_categories: |
| - n<1K |
| task_categories: |
| - other |
| tags: |
| - eeg |
| - affective-computing |
| - memory |
| - brain-decoding |
| - audio-visual-generation |
| --- |
| |
| <div align="center"> |
|
|
| <h1>Revisiting Your Memory: Reconstruction of Affect-Contextualized Memory via EEG-guided Audiovisual Generation</h1> |
|
|
| **ACM Multimedia 2025 CogMAEC Workshop (Oral)** |
|
|
| Joonwoo Kwon*, Heehwan Wang*, Jinwoo Lee*, Sooyoung Kim*, Shinjae Yoo, Yuewei Lin, Jiook Cha |
|
|
| <sub>* equal contribution</sub> |
|
|
| <img src="assets/divider.png" width="100%" height="4"> |
|
|
| ### 💻 Code on GitHub: [github.com/kwxnjoon/Revisiting-Your-Memory](https://github.com/kwxnjoon/Revisiting-Your-Memory) | 📄 Paper: [arXiv 2412.05296](https://arxiv.org/abs/2412.05296) |
|
|
| </div> |
|
|
| <p align="center"><img src="assets/fig1.png" width="100%"></p> |
|
|
| EEG-AffectiveMemory pairs EEG recordings during autobiographical memory recall with the memory's text description, a sketch, an associated musical piece and the participant's affect ratings. It supports the RevisitAffectiveMemory task, which reconstructs affect-contextualized audio-visual memories from EEG. This page holds the data. Code and the pretrained affect extractor are on GitHub. |
|
|
| | Participants | Modalities | Size | |
| |:---:|:---:|:---:| |
| | 10 | EEG (raw + preprocessed), text, sketch, music link, affect | about 0.9 GB | |
|
|
| <img src="assets/divider.png" width="100%" height="4"> |
|
|
| ## Method |
|
|
| <p align="center"><img src="assets/fig2.png" width="100%"></p> |
|
|
| <img src="assets/divider.png" width="100%" height="4"> |
|
|
| ## Files |
|
|
| | Path | Content | |
| |---|---| |
| | `sub-XX/raw/` | Raw EEG of the baseline (`base`) and memory-recall (`task`) sessions (`.edf`, `.easy`, `.nedf`, `.info`) | |
| | `sub-XX/pped/` | Preprocessed EEG, three steps (EEGLAB `.set` / `.fdt`) | |
| | `sub-XX/cebra_input/` | Normalized input of the affect extractor (`.csv`) | |
| | `sub-XX/sub-XX-decoded-matrix.csv` | Decoded affect trajectory | |
| | `sub-XX/sub-XX-sketch.PNG` | Sketch of the recalled memory | |
| | `sub-XX/sub-XX-text.txt` | Memory description and the title and link of the associated musical piece (Korean) | |
| | `text_descriptions.xlsx` | All memory descriptions in one table | |
|
|
| The musical pieces are not included because of copyright. Their titles and links are in `sub-XX-text.txt`. |
|
|
| <img src="assets/divider.png" width="100%" height="4"> |
|
|
| ## Quick start |
|
|
| ### 1. Download the data |
|
|
| ```bash |
| hf download kwonjoon/Revisiting-Your-Memory --repo-type dataset --local-dir EEG_AffectiveMemory_dataset |
| ``` |
|
|
| ### 2. Get the code |
|
|
| ```bash |
| git clone https://github.com/kwxnjoon/Revisiting-Your-Memory.git |
| ``` |
|
|
| Place the downloaded folder at `Revisiting-Your-Memory/EEG_AffectiveMemory_dataset/`. |
|
|
| ### 3. Run |
|
|
| - Affect extraction: `CEBRA(Affect_Extractor)/` (pretrained model included in the GitHub repository). |
| - Image and video generation: `image_video_decoding.ipynb`. |
| - Music generation: put the melody at `sub-XX/sub-XX-melody.wav`, then run `music_generation.ipynb`. |
|
|
| <img src="assets/divider.png" width="100%" height="4"> |
|
|
| ## License |
|
|
| Data under **CC BY-NC 4.0** (non-commercial). The data come from human participants. Use them for research only and do not attempt to identify participants. |
|
|
| <img src="assets/divider.png" width="100%" height="4"> |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{kwon2025revisiting, |
| title = {Revisiting Your Memory: Reconstruction of Affect-Contextualized Memory via EEG-guided Audiovisual Generation}, |
| author = {Kwon, Joonwoo and Wang, Heehwan and Lee, Jinwoo and Kim, Sooyoung and Yoo, Shinjae and Lin, Yuewei and Cha, Jiook}, |
| booktitle = {Proceedings of the 1st International Workshop on Cognition-oriented Multimodal Affective and Empathetic Computing}, |
| pages = {1--10}, |
| year = {2025} |
| } |
| ``` |
|
|
