Update README.md

README.md

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-license: other
-license_name: commercial
-license_link: LICENSE
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+---
+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)