{"dataset":{"id":"61204","dataset_id":"on006923","name":"Dataset of Electroencephalograms of Juvenile Offenders","description":"This dataset contains resting-state EEG recordings from 140 juvenile participants in Colombia, including 74 juvenile offenders and 66 non-offender controls, collected to study neurocognitive patterns associated with delinquency. Recordings were acquired using a 128-channel Biosemi ActiveTwo system during alternating eyes-closed/eyes-open resting-state paradigms. The dataset includes both preprocessed EEG data and extracted spectral power features (mean power, RMS, standard deviation, min/max power, skewness, kurtosis) across delta, theta, alpha, and beta frequency bands for each channel and epoch.","owner_user_id":15,"status":"active","github_repo":"nemarDatasets/on006923","concept_doi":"10.82901/nemar.on006923","latest_version_doi":"10.82901/nemar.on006923.v1.0.0","created_at":"2026-06-29 18:01:44","updated_at":"2026-08-19 00:13:53","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n \"version\": \"2.0\",\n \"pipeline_stage\": \"enriched\",\n \"title\": \"Dataset of Electroencephalograms of Juvenile Offenders\",\n \"description\": \"This dataset contains resting-state EEG recordings from 140 juvenile participants in Colombia, including 74 juvenile offenders and 66 non-offender controls, collected to study neurocognitive patterns associated with delinquency. Recordings were acquired using a 128-channel Biosemi ActiveTwo system during alternating eyes-closed/eyes-open resting-state paradigms. The dataset includes both preprocessed EEG data and extracted spectral power features (mean power, RMS, standard deviation, min/max power, skewness, kurtosis) across delta, theta, alpha, and beta frequency bands for each channel and epoch.\",\n \"methods_description\": \"EEG was recorded using a Biosemi ActiveTwo system with 128 channels plus EOG/ECG at 2048 Hz, online bandpass filtered 0.1-100 Hz. The resting-state paradigm consisted of 12 minutes with 4 minutes alternating eyes closed/open (COCO) and 8 minutes eyes closed (excluded). Preprocessing in EEGLAB/MATLAB included visual inspection, downsampling to 128 Hz, average rereferencing, FIR bandpass filtering (1-40 Hz), bad channel rejection, ASR, and ICA with ICLabel for artifact removal. Spectral features were extracted using Welch's method with 50% overlap and a Hamming window across delta, theta, alpha, and beta bands.\",\n \"license\": \"CC0\",\n \"dataset_type\": \"derivative\",\n \"authors\": {\n \"Aura Polo\": {},\n \"Elmer León-Becerra\": {},\n \"Mariana Pino\": {},\n \"Julie Viloria-Porto\": {},\n \"Elmer León\": {},\n \"Mariana Pino-Melgarejo\": {}\n },\n \"keywords\": [\n {\n \"term\": \"EEG\"\n },\n {\n \"term\": \"Juvenile Delinquency\",\n \"subject_scheme\": \"MeSH\",\n \"value_uri\": \"http://id.nlm.nih.gov/mesh/D007604\"\n },\n {\n \"term\": \"resting-state EEG\"\n },\n {\n \"term\": \"neurocognition\"\n },\n {\n \"term\": \"power spectral density\"\n },\n {\n \"term\": \"adolescent offenders\"\n }\n ],\n \"related_identifiers\": [\n {\n \"identifier\": \"10.18112/openneuro.ds006923.v1.0.1\",\n \"identifier_type\": \"DOI\",\n \"relation_type\": \"IsIdenticalTo\"\n },\n {\n \"identifier\": \"10.18112/openneuro.ds006923.v1.0.0\",\n \"identifier_type\": \"DOI\",\n \"relation_type\": \"IsDerivedFrom\"\n },\n {\n \"identifier\": \"https://github.com/nemarDatasets/on006923\",\n \"identifier_type\": \"URL\",\n \"relation_type\": \"IsDescribedBy\"\n },\n {\n \"identifier\": \"https://nemar.org/dataset/on006923\",\n \"identifier_type\": \"URL\",\n \"relation_type\": \"IsDescribedBy\"\n }\n ],\n \"funding_references\": [\n {\n \"funder_name\": \"SISTEMA GENERAL DE REGALÍAS (SGR)\"\n },\n {\n \"funder_name\": \"MINISTERIO DE CIENCIA TECNOLOGÍA E INNOVACIÓN (MINCIENCIAS)\",\n \"award_number\": \"BPIN 2020000100006\"\n }\n ],\n \"resource_type_general\": \"Dataset\",\n \"resource_type_specific\": \"EEG Dataset\",\n \"modalities\": [\n \"eeg\"\n ],\n \"sizes\": [\n \"8.7 GB (2330 files)\"\n ],\n \"formats\": [\n \".json\",\n \".mat\",\n \".md\",\n \".set\",\n \".tsv\",\n \".xlsx\",\n \".yml\"\n ],\n \"source_hash\": \"356fb047f229d0d609e423c4dec9ceafe85aefc6164d8f9794af9e1e525d06d3\"\n}","last_activity_at":"2026-06-29 18:01:44","source":"openneuro","source_id":"ds006923","subject_count":140,"modalities":"eeg","age_min":14,"age_max":19,"file_size":8731248406,"total_files":3037,"tasks":"restingstate","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Aura Polo, Elmer León-Becerra, Mariana Pino, Julie Viloria-Porto, Elmer León, Mariana Pino-Melgarejo","license":"CC0","readme":"[](https://doi.org/10.82901/nemar.on006923)\n\n# Dataset of Electroencephalograms of Juvenile Offenders\r\n\r\n## Project's name\r\n\r\nDesarrollo 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.\r\n\r\n## Year of project execution\r\n\r\n2021\r\n\r\n## Authors and acknowledgment\r\n\r\nAura Polo, Elmer León, Mariana Pino-Melgarejo and Julie Viloria-Porto.\r\n\r\nRonald Ruiz for his assistance during the data collection process, and Sergio Miranda for his dedication to data processing and cleaning.\r\n\r\n## Work team\r\n\r\n* MAGMA Ingeniería research group\r\n* Hogares Claret foundation\r\n\r\n## Institutions\r\n\r\n- Institución Universitaria de Barranquilla (sede Soledad)\r\n- Universidad del Magdalena\r\n- Universidad Autónoma del Caribe\r\n\r\n## Description\r\n\r\nThis repository contains resting-state EEG data collected with the Biosemi ActiveTwo of 140 participants:\r\n- 74 juvenile offenders (JO)\r\n- 66 juvenile non-offender controls\r\n\r\nExclusion criteria: No psychiatric treatment, dental/orthodontic appliances.\r\n\r\nRecruitment: JO Hogares Claret Foundation (Centro de Reeducación el Oasis & Fundación Luz de Esperanza).\r\n\r\nControls: Institución Nacional de Educación Media INEM Miguel Antonio Caro (Barranquilla).\r\n\r\n## Contents of the dataset\r\n\r\n### Core Files\r\n\r\n- `dataset_description.json`: General information about the study\r\n- `participants.json`: Demographic and group assignment data\r\n- `participants.tsv`: Demographic and group assignment data in table format\r\n\r\n### Features Data (EEG_JO_Dataset/code)\r\n\r\n#### Feature file nomenclature\r\n\r\nFiles are named using the pattern:\r\n`FR_Dats_band_{BAND}_EP_{EYESTATE}_{EPOCH#}_can_{CHANNEL}.xlsx`\r\n\r\n| Component | Example | Description |\r\n|--------------------|-------------|---------------------------------------------------------------------------|\r\n| **FR_Dats_band** | Fixed | Prefix = \"Feature Results Dataset\" |\r\n| **{BAND}** | `ALFA` | EEG frequency band: `ALFA` = Alpha (8-13Hz); `BETA` = Beta (13-30Hz); `DELTA` = Delta (1-4Hz); `THETA` = Theta (4-8Hz) |\r\n| **EP_{EYESTATE}_** | `EP_C_` | Eye state during epoch: `C` = Eyes closed; `O` = Eyes open |\r\n| **{EPOCH#}** | `1` | Epoch number (1 or 2) two epochs per eye state |\r\n| **can_** | Fixed | \"Channel\" prefix |\r\n| **{CHANNEL}** | `A1` | Electrode position (ABCD system): First letter = A • B • C • D
- Number = Electrode ID (1-32) |\r\n\r\n#### File Contents:\r\n\r\nEach Excel file contains 7 features for the specified band/channel/epoch combination:\r\n\r\n1. Mean Power\r\n2. RMS of PSD\r\n3. Standard Deviation\r\n4. Min Power\r\n5. Max Power\r\n6. Skewness\r\n7. Kurtosis\r\n\r\n#### Examples:\r\n\r\n1. `FR_Dats_band_ALFA_EP_C_1_can_A1.xlsx`\r\n - Alpha band features\r\n - First closed-eyes epoch\r\n - Channel A1 (Frontal electrode 1)\r\n\r\n2. `FR_Dats_band_THETA_EP_O_2_can_C15.xlsx`\r\n - Theta band features\r\n - Second open-eyes epoch\r\n - Channel C15 (Posterior electrode 15)\r\n\r\n3. `FR_Dats_band_BETA_EP_C_2_can_B7.xlsx`\r\n - Beta band features\r\n - Second closed-eyes epoch\r\n - Channel B7 (Central electrode 7)\r\n\r\n#### Dataset Structure:\r\n\r\n- 4 epochs per subject:\r\n - 2 closed-eyes: `EP_C_1`, `EP_C_2`\r\n - 2 open-eyes: `EP_O_1`, `EP_O_2`\r\n- 128 channels (A1-D32)\r\n- 4 frequency bands\r\n- Total files per subject: 4 epochs × 128 channels × 4 bands = 2,048 files\r\n\r\n### EEG Data\r\n\r\n```\r\nEEG_JO_Dataset/\r\n├── code/\r\n├── sub-{Subject ID}{Group}/\r\n| ├── eeg/\r\n| | ├── sub-{Subject ID}{Group}_coordsystem.json\r\n| | ├── sub-{Subject ID}{Group}_electrodes.tsv\r\n| | ├── sub-{Subject ID}{Group}_task-{Task Name}_acq-{Datatype}_eeg.json # Epoched data sidecar json\r\n| | ├── sub-{Subject ID}{Group}_task-{Task Name}_acq-{Datatype}_eeg.set # Epoched data\r\n| | ├── sub-{Subject ID}{Group}_task-{Task Name}_channels.tsv\r\n| | ├── sub-{Subject ID}{Group}_task-{Task Name}_desc-{Datatype}_eeg.json # Preprocessed data sidecar json\r\n| | └── sub-{Subject ID}{Group}_task-{Task Name}_desc-{Datatype}_eeg.set # Preprocessed data\r\n├── ...\r\n├── CHANGES\r\n├── dataset_description.json\r\n├── participants.json\r\n├── participants.tsv\r\n└── README.md\r\n```\r\n\r\n#### File Nomenclature\r\n\r\n| Denomination | Value | Description |\r\n|-----------------------|-----------------|------------------------------------------------------------------|\r\n| `sub-` | Fixed \t | Subject prefix |\r\n| `{Subject ID}` | Fixed | **Unique identifier**:
- First digit = group (`1`=sg, `1`=sg2, `2`=cg)
- Last 3 digits = subject ID |\r\n| `{Group}` | `cg`/`sg`/`sg2` | **Group**: `cg`=control, `sg`=study group 1, `sg2`=study group 2 |\r\n| `{Task Name}` | `restingstate` | **Task name** (resting state) |\r\n| `acq-` `desc-` | `acq-`/`desc-` | **Label**: `acq-` = acquisition, `desc-` = description |\r\n| `{Datatype}` | `epochs`/`preprocessed` | Adquisition type |\r\n| `eeg` | Electroencephalography data | Data type |\r\n| Extension | `.set` | **File type**: processed |\r\n\r\n#### Examples\r\n\r\n1. `sub-1005sg_task-restingstate_acq-epochs_eeg.set` = Epochs EEG for **study group 1** subject 005 (full ID 1005)\r\n2. `sub-1005sg_task-restingstate_desc-preprocessing_eeg.set` = Preprocessed EEG for **study group 1** subject 005 (full ID 1005)\r\n\r\n## Methods\r\n\r\n### EEG Acquisition\r\n\r\n- **Device**: Biosemi ActiveTwo system\r\n- **Electrodes**: 128 channels (radial placement, 10-20 system reference)\r\n- **Additional channels**: EOG, ECG recorded\r\n- **Sampling rate**: 2048 Hz (downsampled to 128 Hz during preprocessing)\r\n- **Online filtering**: 0.1-100 Hz bandpass\r\n- **Setup**:\r\n - Participants seated awake\r\n - Continuous monitoring for movements/sleep\r\n - Event markers via serial communication (paradigm triggers)\r\n\r\n### Paradigms\r\n\r\n*(Dataset contains only resting-state recordings)*\r\n\r\n- **Resting State (RS)**:\r\n - Total duration: 12 minutes\r\n - Sequence:\r\n - 4 min alternating eyes closed/open (COCO: Closed-Open-Closed-Open)\r\n - 8 min eyes closed (excluded from current dataset)\r\n- **Segment trimming**:\r\n - 5s post-event onset\r\n - 5s pre-event offset (to avoid transition artifacts)\r\n\r\n### Preprocessing pipeline (EEGLAB/MATLAB)\r\n\r\n1. **Visual inspection**:\r\n - Raw data review using BDFreader\r\n - Identification of bad channels/artifacts\r\n2. **Downsampling**:\r\n - 2048 Hz → 128 Hz (resting-state data)\r\n3. **Rereferencing**:\r\n - Average reference (replaced failed earlobe reference)\r\n4. **Filtering**:\r\n - Bandpass FIR: 1-40 Hz\r\n - High-pass: 1 Hz (0.5 Hz cutoff, 425 points)\r\n - Low-pass: 40 Hz (45 Hz cutoff, 45 points)\r\n5. **Artifact Removal**:\r\n - Bad channel rejection:\r\n - Flat signals > 5s\r\n - SD > 4\r\n - Correlation < 0.8 with neighbors\r\n - ASR (Artifact Subspace Reconstruction)\r\n - ICA + ICLabel (components >90% non-brain removed)\r\n\r\n### Feature Extraction\r\n\r\n- **PSD Calculation**: Welch's method (50% overlap, Hamming window)\r\n- **Frequency bands**:\r\n - Delta (δ): 1-4 Hz\r\n - Theta (θ): 4-8 Hz\r\n - Alpha (α): 8-13 Hz\r\n - Beta (β): 13-30 Hz\r\n- **Features per band/channel**:\r\n 1. Mean Power\r\n 2. RMS of PSD\r\n 3. Standard Deviation\r\n 4. Minimum Power\r\n 5. Maximum Power\r\n 6. Skewness\r\n 7. 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