README.md

---
  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.