EEGdash›OpenNeuro›DS006923
Iss. 6923 · 140 subjects · 280 recordings · CC0
Dataset Brief · Dataset of Electroencephalograms of Juvenile Offenders

DS006923: eeg dataset, 140 subjects#

Dataset of Electroencephalograms of Juvenile Offenders

Citation: Aura Polo, Elmer León, Mariana Pino-Melgarejo, Julie Viloria-Porto (20). Dataset of Electroencephalograms of Juvenile Offenders. 10.18112/openneuro.ds006923.v1.0.0

140-participant EEG dataset — Dataset of Electroencephalograms of Juvenile Offenders.

Data & curation Aura Polo · Elmer León · Mariana Pino-Melgarejo · Julie Viloria-Porto
Year 20 · Distributed via OpenNeuro
Funding SISTEMA GENERAL DE REGALÍAS (SGR) · MINISTERIO DE CIENCIA TECNOLOGÍA E INNOVACIÓN (MINCIENCIAS) BPIN 2020000100006
EEG · 128 ch128 HzBIDS 1.0.0Task · restingstateOtherResting StateClinical/Intervention
Layer 01Study
What was asked
Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.
Layer 02Signal · BIDS
What was recorded
Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.
Layer 03Training · ML
What you can train on
Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.
§ 01Access · Get started

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006923

dataset = DS006923(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS006923(cache_dir="./data", subject="01")

Advanced query

dataset = DS006923(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{ds006923,
  title = {Dataset of Electroencephalograms of Juvenile Offenders},
  author = {Aura Polo and Elmer León and Mariana Pino-Melgarejo and Julie Viloria-Porto},
  doi = {10.18112/openneuro.ds006923.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds006923.v1.0.0},
}
§ 02Study · The README

About This Dataset#

Desarrollo de un sistema inteligente multiparamétrico para el reconocimiento de patrones asociados a disfunciones neurocognitivas en jóvenes en conflicto con la ley en el departamento del Atlántico.

2021

Dataset of Electroencephalograms of Juvenile Offenders

Project’s name

Authors and acknowledgment

Aura Polo, Elmer León, Mariana Pino-Melgarejo and Julie Viloria-Porto.

Ronald Ruiz for his assistance during the data collection process, and Sergio Miranda for his dedication to data processing and cleaning.

View full README

Dataset of Electroencephalograms of Juvenile Offenders

Project’s name

Authors and acknowledgment

Aura Polo, Elmer León, Mariana Pino-Melgarejo and Julie Viloria-Porto.

Ronald Ruiz for his assistance during the data collection process, and Sergio Miranda for his dedication to data processing and cleaning.

Work team

* MAGMA Ingeniería research group * Hogares Claret foundation

Institutions

  • Institución Universitaria de Barranquilla (sede Soledad)

  • Universidad del Magdalena

  • Universidad Autónoma del Caribe

Description

This repository contains resting-state EEG data collected with the Biosemi ActiveTwo of 140 participants: - 74 juvenile offenders (JO) - 66 juvenile non-offender controls

Exclusion criteria: No psychiatric treatment, dental/orthodontic appliances.

Recruitment: JO Hogares Claret Foundation (Centro de Reeducación el Oasis & Fundación Luz de Esperanza). Controls: Institución Nacional de Educación Media INEM Miguel Antonio Caro (Barranquilla).

Contents of the dataset

Core Files

  • dataset_description.json: General information about the study

  • participants.json: Demographic and group assignment data

  • participants.tsv: Demographic and group assignment data in table format

Features Data (EEGJODataset/code)

Feature file nomenclature

Files are named using the pattern: FR_Dats_band_{BAND}_EP_{EYESTATE}_{EPOCH#}_can_{CHANNEL}.xlsx

| Component          | Example     | Description                                                               |
|--------------------|-------------|---------------------------------------------------------------------------|
| **FR_Dats_band**   | Fixed       | Prefix = "Feature Results Dataset"                                        |
| **{BAND}**         | `ALFA`      | EEG frequency band: `ALFA` = Alpha (8-13Hz); `BETA` = Beta (13-30Hz); `DELTA` = Delta (1-4Hz); `THETA` = Theta (4-8Hz)                                                               |
| **EP_{EYESTATE}_** | `EP_C_`     | Eye state during epoch: `C` = Eyes closed; `O` = Eyes open                |
| **{EPOCH#}**       | `1`         | Epoch number (1 or 2) two epochs per eye state                            |
| **can_**           | Fixed       | "Channel" prefix                                                          |

| **{CHANNEL}**      | `A1`        | Electrode position (ABCD system): First letter = A • B • C • D

  • Number = Electrode ID (1-32) |

File Contents:

Each Excel file contains 7 features for the specified band/channel/epoch combination: 1. Mean Power 2. RMS of PSD 3. Standard Deviation 4. Min Power 5. Max Power 6. Skewness 7. Kurtosis

Examples:

  1. FR_Dats_band_ALFA_EP_C_1_can_A1.xlsx - Alpha band features - First closed-eyes epoch - Channel A1 (Frontal electrode 1)

  2. FR_Dats_band_THETA_EP_O_2_can_C15.xlsx - Theta band features - Second open-eyes epoch - Channel C15 (Posterior electrode 15)

  3. FR_Dats_band_BETA_EP_C_2_can_B7.xlsx - Beta band features - Second closed-eyes epoch - Channel B7 (Central electrode 7)

Dataset Structure:

  • 4 epochs per subject: - 2 closed-eyes: EP_C_1, EP_C_2 - 2 open-eyes: EP_O_1, EP_O_2

  • 128 channels (A1-D32)

  • 4 frequency bands

  • Total files per subject: 4 epochs × 128 channels × 4 bands = 2,048 files

EEG Data

EEG_JO_Dataset/

├── code/
├── sub-{Subject ID}{Group}/
|   ├── eeg/
|   |   ├── sub-{Subject ID}{Group}_coordsystem.json
|   |   ├── sub-{Subject ID}{Group}_electrodes.tsv
|   |   ├── sub-{Subject ID}{Group}_task-{Task Name}_acq-{Datatype}_eeg.json # Epoched data sidecar json
|   |   ├── sub-{Subject ID}{Group}_task-{Task Name}_acq-{Datatype}_eeg.set # Epoched data
|   |   ├── sub-{Subject ID}{Group}_task-{Task Name}_channels.tsv
|   |   ├── sub-{Subject ID}{Group}_task-{Task Name}_desc-{Datatype}_eeg.json # Preprocessed data sidecar json
|   |   └── sub-{Subject ID}{Group}_task-{Task Name}_desc-{Datatype}_eeg.set # Preprocessed data
├── ...
├── CHANGES
├── dataset_description.json
├── participants.json
├── participants.tsv
└── README.md

File Nomenclature

| Denomination          | Value           | Description                                                      |
|-----------------------|-----------------|------------------------------------------------------------------|
| `sub-`                | Fixed            | Subject prefix                                                   |

| `{Subject ID}`        | Fixed           | **Unique identifier**:

  • First digit = group (``1``=sg, ``1``=sg2, ``2``=cg)

  • Last 3 digits = subject ID |

| `{Group}`             | `cg`/`sg`/`sg2` | **Group**: `cg`=control, `sg`=study group 1, `sg2`=study group 2 |
| `{Task Name}`         | `restingstate`  | **Task name** (resting state)                                    |
| `acq-` `desc-`        | `acq-`/`desc-`  | **Label**: `acq-` = acquisition, `desc-` = description           |
| `{Datatype}`          | `epochs`/`preprocessed` | Adquisition type                                         |
| `eeg`                 | Electroencephalography data | Data type                                            |
| Extension             | `.set`          | **File type**: processed                                         |

Examples

  1. sub-1005sg_task-restingstate_acq-epochs_eeg.set = Epochs EEG for study group 1 subject 005 (full ID 1005)

  2. sub-1005sg_task-restingstate_desc-preprocessing_eeg.set = Preprocessed EEG for study group 1 subject 005 (full ID 1005)

Methods

EEG Acquisition

  • Device: Biosemi ActiveTwo system

  • Electrodes: 128 channels (radial placement, 10-20 system reference)

  • Additional channels: EOG, ECG recorded

  • Sampling rate: 2048 Hz (downsampled to 128 Hz during preprocessing)

  • Online filtering: 0.1-100 Hz bandpass

  • Setup: - Participants seated awake - Continuous monitoring for movements/sleep - Event markers via serial communication (paradigm triggers)

Paradigms

(Dataset contains only resting-state recordings) - Resting State (RS):

  • Total duration: 12 minutes

  • Sequence: - 4 min alternating eyes closed/open (COCO: Closed-Open-Closed-Open) - 8 min eyes closed (excluded from current dataset)

  • Segment trimming:

    • 5s post-event onset

    • 5s pre-event offset (to avoid transition artifacts)

Preprocessing pipeline (EEGLAB/MATLAB)

  1. Visual inspection: - Raw data review using BDFreader - Identification of bad channels/artifacts

  2. Downsampling: - 2048 Hz → 128 Hz (resting-state data)

  3. Rereferencing: - Average reference (replaced failed earlobe reference)

  4. Filtering: - Bandpass FIR: 1-40 Hz - High-pass: 1 Hz (0.5 Hz cutoff, 425 points) - Low-pass: 40 Hz (45 Hz cutoff, 45 points)

  5. Artifact Removal: - Bad channel rejection:

    • Flat signals > 5s

    • SD > 4

    • Correlation < 0.8 with neighbors

    • ASR (Artifact Subspace Reconstruction)

    • ICA + ICLabel (components >90% non-brain removed)

Feature Extraction

  • PSD Calculation: Welch’s method (50% overlap, Hamming window)

  • Frequency bands: - Delta (δ): 1-4 Hz - Theta (θ): 4-8 Hz - Alpha (α): 8-13 Hz - Beta (β): 13-30 Hz

  • Features per band/channel: 1. Mean Power 2. RMS of PSD 3. Standard Deviation 4. Minimum Power 5. Maximum Power 6. Skewness 7. Kurtosis

  • Feature volume: 14,336 features/subject (4 bands × 128 channels × 4 segments × 7 features)

Technical Specifications

  • Processing Hardware: - Intel Core i5-9400F @2.9GHz - 16GB RAM - Windows 10 (64-bit)

  • Software: - MATLAB 2020a - EEGLAB toolbox - Python (scikit-learn, pandas for feature selection)

  • Processing Time: ~10 minutes/subject

Funding

This research was funded by the SISTEMA GENERAL DE REGALÍAS - SGR and the MINISTERIO DE CIENCIA TECNOLOGÍA E INNOVACIÓN - MINCIENCIAS from Colombia, in the framework of the project “Desarrollo de un sistema inteligente multiparamétrico para el reconocimiento de patrones asociados a disfunciones neurocognitivas en jóvenes en conflicto con la ley en el departamento del Atlántico”, with grant number BPIN 2020000100006.

Support

Correspondence: Aura Polo (apolol@unimagdalena.edu.co); Elmer León (elmerleondb@unimagdalena.edu.co); Julie Viloria-Porto (julieviloriapp@unimagdalena.edu.co)

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution (n=140, range 14–19 yr, mean 16.7 yr · sex per subject not reported)

1015

Sex composition

140
subjects
Male
140

Channel counts: 128 ch (n=280 recordings)

Sampling frequencies: 128.0 Hz (n=280 recordings)

Total recording duration: 37 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 128 ch · EEG · 128 Hz · 140 subjects, 280 recordings
Live trace viewer — sub-2012cg · task-restingstate

Showing one representative recording out of 140 subjects and 280 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

Electrode layout — EEG · 128 sensors — 128 channels

NEMAR Processing Statistics#

The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.

NEMAR pipeline statistics — DS006923
HED event descriptors word cloud HED event descriptors word cloud — DS006923
§ 05Manifest · BIDS tree

Manifest#

File Explorer#

Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.

Recordings—
Files—
Subjects—
Modalities—
Click to load file structure…
Full dataset metadata table

Dataset ID

DS006923

Title

Dataset of Electroencephalograms of Juvenile Offenders

Author (year)

Polo2025

Canonical

—

Importable as

DS006923, Polo2025

Year

20

Authors

Aura Polo, Elmer León, Mariana Pino-Melgarejo, Julie Viloria-Porto

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds006923.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds006923,
  title = {Dataset of Electroencephalograms of Juvenile Offenders},
  author = {Aura Polo and Elmer León and Mariana Pino-Melgarejo and Julie Viloria-Porto},
  doi = {10.18112/openneuro.ds006923.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds006923.v1.0.0},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.DS006923(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Polo2025
Canonical—
Importable asDS006923 · Polo2025
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.DS006923(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Dataset of Electroencephalograms of Juvenile Offenders

Study:

ds006923 (OpenNeuro)

Author (year):

Polo2025

Canonical:

—

Also importable as: DS006923, Polo2025.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Other. Subjects: 140; recordings: 280; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir#

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query#

Merged query with the dataset filter applied.

Type:

dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006923 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006923 DOI: https://doi.org/10.18112/openneuro.ds006923.v1.0.0

Examples

>>> from eegdash.dataset import DS006923
>>> dataset = DS006923(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
save(path: str, overwrite: bool = False, offset: int = 0)[source]#

Save datasets to files by creating one subdirectory for each dataset:

path/
    0/
        0-raw.fif | 0-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)
    1/
        1-raw.fif | 1-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)
Parameters:

  • path (str) –

    Directory in which subdirectories are created to store

    -raw.fif | -epo.fif and .json files to.

  • overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.

  • offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.

Access modesMNE → braindecode → PyTorch → ML
.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne
BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode
DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch
Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr
Hugging FacePre-bundled mirror at EEGDash/ds006923 · pull with datasets.load_dataset("EEGDash/ds006923").huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — DS006923.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons
Examples using EEGDashcurated · start here
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.Load one EEG recordingResolve a single record to an MNE Raw with channels and events.EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.

Swap any load_dataset(...) call for ds006923 to reproduce the tutorial on this dataset.

Citation

Aura Polo, Elmer León, Mariana Pino-Melgarejo, Julie Viloria-Porto (20). Dataset of Electroencephalograms of Juvenile Offenders. 10.18112/openneuro.ds006923.v1.0.0

Provenance

¹Contributed to openneuro in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.18112/openneuro.ds006923.v1.0.0.

BIDS
BIDS 1.0.0
Sidecars
electrodes · coordsystem · eeg.json
Machine-readable
schema.org/Dataset · Croissant

See Also#