DS006923#

Dataset of Electroencephalograms of Juvenile Offenders

Access recordings and metadata through EEGDash.

Citation: Aura Polo, Elmer León, Mariana Pino-Melgarejo, Julie Viloria-Porto (2025). Dataset of Electroencephalograms of Juvenile Offenders. 10.18112/openneuro.ds006923.v1.0.0

Modality: eeg Subjects: 140 Recordings: 985 License: CC0 Source: openneuro

Metadata: Complete (100%)

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},
}

About This Dataset#

Dataset of Electroencephalograms of Juvenile Offenders

Project’s name

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.

View full README

Dataset of Electroencephalograms of Juvenile Offenders

Project’s name

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.

Year of project execution

2021

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)

Dataset Information#

Dataset ID

DS006923

Title

Dataset of Electroencephalograms of Juvenile Offenders

Year

2025

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},
}

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

  • Subjects: 140

  • Recordings: 985

  • Tasks: 1

Channels & sampling rate

  • Channels: 128

  • Sampling rate (Hz): 128.0

  • Duration (hours): 0.0

Tags

  • Pathology: Other

  • Modality: Resting State

  • Type: Clinical/Intervention

Files & format

  • Size on disk: 8.1 GB

  • File count: 985

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds006923.v1.0.0

Provenance

API Reference#

Use the DS006923 class to access this dataset programmatically.

class eegdash.dataset.DS006923(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006923. 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

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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

  • path (str or Path) – Destination file path.

  • overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

See Also#