EEG-AffectiveMemory dataset (from Revisiting-Your-Memory)

README.md

@@ -0,0 +1,107 @@
+---
+license: cc-by-nc-4.0
+language:
+- en
+- ko
+pretty_name: EEG-AffectiveMemory
+size_categories:
+- n<1K
+task_categories:
+- other
+tags:
+- eeg
+- affective-computing
+- memory
+- brain-decoding
+- audio-visual-generation
+---
+
+<div align="center">
+
+<h1>EEG-AffectiveMemory: Dataset for Revisiting Your Memory</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/ioahKwon/Revisiting-Your-Memory](https://github.com/ioahKwon/Revisiting-Your-Memory) &nbsp;|&nbsp; 📄 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/EEG-AffectiveMemory --repo-type dataset --local-dir EEG_AffectiveMemory_dataset
+```
+
+### 2. Get the code
+
+```bash
+git clone https://github.com/ioahKwon/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}
+}
+```

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sub-01/sub-01-text.txt

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+(Kor.)
+
+(Associated Musical Pieces) 유투브, 'JVKE-Golden Hour | Piano Cover 피아노 커버' (https://youtu.be/cIT1QRHPtvc?si=UtWNyI6ftDwl9c7N)
+
+(Text Description) 다리 위에서의 이미지. 동이 트고 있으며 바람이 많이 불어 머리가 흩날리는 여성의 뒷모습이 보임. 저 멀리 해가 천천히 떠오름. 도시적인 이미지가 강함. 골든 아워. 다른 다리가 보이고, 사람들이 몇몇 보임. 시간은 오전 6시 이전. 공기가 차가워보이지만 그와 대비되는 태양의 따스함이 돋보임. 온 세상이 주황빛으로 물들고 그것을 바라보는 사람. 하늘, 태양, 다리 그리고 사람들이 보임. 움직이는 사람들 속 가만히 태양이 떠오르는 것을 보고 있는 한 사람. 일출과 도시적인 배경이 어우러짐.
+
+------------------------------------------------------------------------------------------------------------------
+
+(Eng.)
+
+(Associated Musical Pieces) Youtube, 'JVKE - Golden Hour | Piano Cover 피아노커버' (https://youtu.be/cIT1QRHPtvc?si=UtWNyI6ftDwl9c7N)
+
+(Text Description) Photorealistic urban sunrise scene on a bridge, featuring a woman viewed from behind with her hair flowing dramatically in the strong morning wind. The composition captures the precise moment of daybreak, around 6 AM, as the sun slowly emerges on the horizon between city buildings, casting everything in rich golden hour light. The urban landscape is dominated by multiple bridges creating strong geometric lines across the frame, while scattered early morning commuters move across the scene in the distance. The lighting creates a striking contrast - the crisp, cold morning air is visible through a slight atmospheric haze, while the warm sunlight bathes everything in brilliant orange tones. The main subject stands still against the dynamic cityscape, her silhouette perfectly framed against the rising sun, while her hair dances in the morning breeze. Every element emphasizes the urban dawn - from the way the golden light catches the architectural elements and creates long shadows across the bridge's surface, to how the city skyline frames the emergence of the sun. The composition captures that transformative moment where night yields to day, creating a perfect harmony between natural and urban elements. The contrast between the cool morning air and warm sunlight is palpable, while the orange glow transforms the urban setting into a canvas of light and shadow. The scene precisely balances dynamic movement (flowing hair, walking commuters) with stillness (the woman's contemplative pose), all set against the backdrop of a city awakening to a new day.

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+(Kor.)
+
+(Associated Musical Pieces) 유튜브, '원슈타인, 적외선 카메라 - Piano Cover' (https://youtu.be/wW4aeAVVHFE?si=Tqm7NcJIe1yT3NH3)
+
+(Text Description) 아무 소리도 들리지 않는 느지막한 밤 시간에 창 밖으로는 검은 어둠이 내려앉았고, 방 안에서 하얀 책상 위 은색 노트북에는 뮤직비디오가 틀어져 있습니다. 빨간색, 노란색, 초록색, 파란색이 고루 섞인 사람의 실루엣이 영상 안에서 계속해서 움직입니다. 얼핏 보면 불꽃이 일렁이는 듯하고 자세히 보면 노래하는 가수의 움직임이 보입니다.
+
+------------------------------------------------------------------------------------------------------------------
+
+(Eng.)
+
+(Associated Musical Pieces) Youtube, 'Wonstein, Infrared Camera - Piano Cover' (https://youtu.be/wW4aeAVVHFE?si=Tqm7NcJIe1yT3NH3)
+
+(Text Description) Photorealistic late-night interior scene capturing a quiet room where complete silence meets digital animation. The composition centers on a silver laptop sitting on a pristine white desk, its screen providing the sole source of illumination against the absolute darkness visible through the window. On the screen, a mesmerizing music video plays, featuring a dynamic human silhouette rendered in a harmonious blend of red, yellow, green, and blue colors. The figure's movements create a hypnotic display that appears at first glance like dancing flames, but upon closer inspection reveals itself as the fluid motions of a performing artist. The lighting creates dramatic contrast - the laptop's screen casts a soft glow that illuminates the immediate desk area while the rest of the room fades into deep shadows. The window reveals nothing but pure blackness outside, emphasizing the intimate isolation of the scene. Every element emphasizes the interplay between darkness and digital light - from the way the screen's colors reflect subtly on the desk's white surface to how the blackness beyond the window creates a perfect frame for the illuminated scene. The composition captures that unique moment of late-night solitude where digital art becomes a window into another world. The multicolored figure on screen appears to dance between abstraction and reality, its movements creating patterns that blur the line between human performance and abstract art. The scene balances technological precision (the sleek laptop, the clear screen) with artistic fluidity (the colorful, moving figure), all set against the canvas of night's darkness.