DBbun/EEG-250Hz_v1.0

DBbun — Synthetic Scalp EEG (10–20 @ 250 Hz)

Overview

DBbun EEG is a 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.

This work was inspired by the following study: Roy S, Kiral I, Mirmomeni M, Mummert T, Braz A, Tsay J, Tang J, Asif U, Schaffter T, Ahsen ME, Iwamori T, Yanagisawa H, Poonawala H, Madan P, Qin Y, Picone J, Obeid I, Marques BA, Maetschke S, Khalaf R, Rosen-Zvi M, Stolovitzky G, Harrer S; IBM Epilepsy Consortium. Evaluation of artificial intelligence systems for assisting neurologists with fast and accurate annotations of scalp electroencephalography data. EBioMedicine. 2021 Apr;66:103275. doi: 10.1016/j.ebiom.2021.103275. Epub 2021 Mar 18. PMID: 33745882; PMCID: PMC8105505.

---

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.

---

Comparison with Other Synthetic EEG Datasets

A variety of research and open projects have proposed methods for generating synthetic EEG, but very few provide long, continuous, multi-channel signals with structured metadata.
The table below summarizes key characteristics of known efforts compared to DBbun EEG.

Feature	Existing Synthetic EEG Datasets	DBbun EEG
Continuous multichannel raw EEG signals	Rare – most provide only extracted features or short samples	Yes – full 10–20 recordings (≈1 hour each)
Per-second or sample-level annotations	Usually absent or limited to class-level labels	Yes – one label per second (ictal / non-ictal)
Number and duration of sessions	Typically fewer than 10 sessions, often only a few minutes each	250 sessions totaling over 250 hours
Validation and realism metrics	Often qualitative or absent	Full quantitative and visual validation
Montage and channel structure	Often single-channel or unspecified	Standard 10–20 + TCP differential montage
Licensing and accessibility	Often research-only or restricted	Commercial license with clear permitted use
Intended applications	Algorithm demos or augmentation	Training, validation, and commercial prototyping

This comparison highlights DBbun EEG’s unique position as a large-scale, validated, and legally distributable synthetic EEG dataset.

---

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)