---
license: other
license_name: commercial
license_link: LICENSE
---
# DBbun — Synthetic Scalp EEG (10–20 @ 250 Hz)

## Overview
DBbun EEG is a professionally generated collection of synthetic scalp EEG recordings that replicate the structure and statistical properties of real clinical EEG while remaining fully privacy-safe.

Each file contains multi-channel 10–20 EEG sampled at 250 Hz, with per-second seizure labels and optional TCP (bipolar) differential channels.  
The dataset enables research and commercial development in seizure detection, artifact rejection, biosignal modeling, and algorithm benchmarking, without any regulatory or privacy restrictions.

All content is synthetically generated and contains no patient or institutional data.

---

## Dataset Composition

### Splits
| Split | Records | Duration | Purpose |
|:------|:--------:|:---------:|:--------|
| train/ | 200 | approximately 200 hours | Model training |
| valid/ | 25 | approximately 25 hours | Hyperparameter tuning |
| test/  | 25 | approximately 25 hours | Evaluation |

Each split includes:
- `patient_XXXXXX.npz` — EEG waveform and label arrays  
- `patient_XXXXXX_events.json` — seizure interval metadata  
- `metadata.csv` — session-level descriptors  

### File contents
| Key | Type | Shape | Description |
|:----|:-----|:------|:------------|
| `eeg` | float32 | [n_channels, n_samples] | Scalp EEG waveforms (10–20 layout ± TCP montage) |
| `sr` | int | — | Sampling rate (250 Hz) |
| `channels` | list[str] | [n_channels] | Channel names |
| `labels_sec` | uint8 | [seconds] | 0 = non-ictal, 1 = ictal |

---

## Validation and Quality Assurance

### Quantitative validation
Descriptive realism metrics were computed using the included notebook `DBbun_EEG_Validation.ipynb`, which summarizes power ratios, durations, and seizure prevalence.  
Results are saved in `validation/realism_report.md`.

Key findings:
| Metric | Mean | Std | Description |
|:--------|-----:|----:|:------------|
| Alpha ratio | 0.496 | 0.020 | Balanced 8–13 Hz band typical of resting EEG |
| Delta ratio | 0.428 | 0.021 | Realistic low-frequency power |
| Channels | 38 | 0 | 20 raw + 18 TCP differential channels |
| Duration (s) | 3469 | 514 | Approximately 58 minutes per record |
| Seizure fraction | 0.0126 | 0.012 | Approximately 1.3% ictal content |

Validation confirmed:
- Stable 10–20 montage structure  
- Physiologically plausible alpha/delta ratios and amplitudes  
- Realistic seizure sparsity and variability  
- Clean baselines without abnormal drift  
- Consistent metadata across all splits

### Visual previews
Representative 10-second EEG windows were exported as `previews/*.png`.  
Each shows realistic amplitude, rhythmic composition, and spectral balance.

---

## How to Use

### Load one record
```python
import numpy as np

z = np.load("data/train/patient_000001.npz")
eeg = z["eeg"]
sr = int(z["sr"])
channels = [c.decode() if isinstance(c, bytes) else c for c in z["channels"]]
labels = z["labels_sec"]

print("Channels:", len(channels))
print("Shape:", eeg.shape)
print("Duration (s):", eeg.shape[1] / sr)