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license: cc-by-4.0
pretty_name: EEGFaceSem
tags:
- eeg
- bci
- rsvp
- face-stimuli
- generative-latents
size_categories:
- 10K<n<100K
---
# EEGFaceSem
EEG Dataset for Semantic Visual Response
## Installation
```bash
git clone https://huggingface.co/datasets/yefllower/EEGFaceSem
cd EEGFaceSem
pip install -e .
```
## Quick Start
```python
import EEGFaceSem
# Auto-downloads data when loading
X, Y, ids = EEGFaceSem.load_data(task='female')
print(f"X: {X.shape}, Y: {Y.shape}") # X: (n_trials, 32, 1101), Y: (n_trials,)
# Or download specific subjects only (~300MB per subject)
# EEGFaceSem.download(subjects=[1])
```
### Image Generation
```python
# Load latent vectors and generate face image
image_ids, latents, id_to_idx = EEGFaceSem.load_latent()
EEGFaceSem.generate(latents[0:1])[0].save("face.png")
```
## Dataset Info
| Metric | Value |
|--------|-------|
| Subjects | 30 |
| Total epochs | 64,124 |
| EEG channels | 32 |
| Sampling rate | 1000 Hz |
| Epoch window | [-0.2, 0.9]s |
### 8 Tasks
| Task ID | Task Name |
|---------|-----------|
| 0 | female |
| 1 | male |
| 2 | blond |
| 3 | darkhaired |
| 4 | smiles |
| 5 | nosmile |
| 6 | old |
| 7 | young |
## API Reference
### Data Loading
```python
# Download data
EEGFaceSem.download(data_type="processed") # or "raw", "both"
EEGFaceSem.download(subjects=[1, 2, 3]) # specific subjects only
# Load data
X, Y, ids = EEGFaceSem.load_data(task='female')
```
### Splitting
```python
# Random split
(X_train, Y_train), (X_test, Y_test) = EEGFaceSem.split_random(X, Y, test_size=0.2)
# Leave-one-subject-out
(X_train, Y_train), (X_test, Y_test) = EEGFaceSem.split_by_subject(X, Y, ids, test_subject=1)
```
### Benchmarking
```python
EEGFaceSem.benchmark(
model='LDA', # LDA, LR, MLP, EEGNet, EEGPT
task_id=0, # 0-7 or -1 for all
strategy='single_subject',
)
```
### Image Generation
```python
# Load latent vectors
image_ids, latents, id_to_idx = EEGFaceSem.load_latent()
# Generate from latent
images = EEGFaceSem.generate(latents[0:1])
images[0].save("face.png")
# Generate from specific image ID in data
img_id = int(ids[0, 4])
EEGFaceSem.generate(latents[id_to_idx[img_id]:id_to_idx[img_id]+1])[0].save(f"face_{img_id}.png")
```
## License
The dataset is released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). The accompanying benchmark code is released under the Apache License 2.0.
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