README.md

metadata

license: other
license_name: commercial
license_link: LICENSE
tags:
  - eeg
  - synthetic-data
  - biosignals
  - healthcare
  - neuroscience
  - machine-learning
  - deep-learning
  - time-series
  - seizure-detection
  - real-world-evidence
  - data-generation
  - biomedical
  - brain-signals
  - artificial-data
  - education
size_categories:
  - 1M<n<10M
task_categories:
  - time-series-forecasting
language:
  - en
pretty_name: DBbun Synthetic EEG (10–20 @ 250 Hz)

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.

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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

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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.

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How to Use

Load one record

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)