{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a3476","dataset_id":"ds006923","associated_paper_doi":null,"authors":["Aura Polo","Elmer León","Mariana Pino-Melgarejo","Julie Viloria-Porto"],"bids_version":"1.0.0","contact_info":["Elmer David  León Becerra"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds006923.v1.0.0","datatypes":["eeg"],"demographics":{"subjects_count":140,"ages":[15,15,18,18,16,17,15,16,16,16,15,17,15,15,15,15,17,16,14,16,16,16,18,14,15,18,15,17,15,16,17,16,17,16,15,16,16,18,17,18,15,18,17,17,16,18,17,16,17,17,18,17,17,18,18,17,16,16,16,17,16,16,18,17,15,16,19,18,17,19,19,18,18,18,19,17,18,16,18,17,17,18,19,16,19,15,18,18,17,18,18,18,17,17,16,15,17,16,18,17,16,17,17,15,17,17,18,17,18,17,17,17,15,16,16,18,17,17,16,17,18,17,17,16,16,17,15,18,17,16,15,17,15,16,15,18,18,18,16,16],"age_min":14,"age_max":19,"age_mean":16.714285714285715,"species":null,"sex_distribution":{"m":140},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds006923","osf_url":null,"github_url":null,"paper_url":null},"funding":["SISTEMA GENERAL DE REGALÍAS (SGR)","MINISTERIO DE CIENCIA TECNOLOGÍA E INNOVACIÓN (MINCIENCIAS) BPIN 2020000100006"],"ingestion_fingerprint":"4af72a7c0f318eed9d3626dc1fd71f3de1a405088f3e6ae4dccb3cf729b52768","license":"CC0","n_contributing_labs":null,"name":"Dataset of Electroencephalograms of Juvenile Offenders","readme":"# Dataset of Electroencephalograms of Juvenile Offenders\n## Project's name\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.\n## Year of project execution\n2021\n## Authors and acknowledgment\nAura Polo, Elmer León, Mariana Pino-Melgarejo and Julie Viloria-Porto.\nRonald Ruiz for his assistance during the data collection process, and Sergio Miranda for his dedication to data processing and cleaning.\n## Work team\n* MAGMA Ingeniería research group\n* Hogares Claret foundation\n## Institutions\n- Institución Universitaria de Barranquilla (sede Soledad)\n- Universidad del Magdalena\n- Universidad Autónoma del Caribe\n## Description\nThis repository contains resting-state EEG data collected with the Biosemi ActiveTwo of 140 participants:\n- 74 juvenile offenders (JO)\n- 66 juvenile non-offender controls\nExclusion criteria: No psychiatric treatment, dental/orthodontic appliances.\nRecruitment: JO Hogares Claret Foundation (Centro de Reeducación el Oasis & Fundación Luz de Esperanza).\nControls: Institución Nacional de Educación Media INEM Miguel Antonio Caro (Barranquilla).\n## Contents of the dataset\n### Core Files\n- `dataset_description.json`: General information about the study\n- `participants.json`: Demographic and group assignment data\n- `participants.tsv`: Demographic and group assignment data in table format\n### Features Data (EEG_JO_Dataset/code)\n#### Feature file nomenclature\nFiles are named using the pattern:\n`FR_Dats_band_{BAND}_EP_{EYESTATE}_{EPOCH#}_can_{CHANNEL}.xlsx`\n| Component          | Example     | Description                                                               |\n|--------------------|-------------|---------------------------------------------------------------------------|\n| **FR_Dats_band**   | Fixed       | Prefix = \"Feature Results Dataset\"                                        |\n| **{BAND}**         | `ALFA`      | EEG frequency band: `ALFA` = Alpha (8-13Hz); `BETA` = Beta (13-30Hz); `DELTA` = Delta (1-4Hz); `THETA` = Theta (4-8Hz)                                                               |\n| **EP_{EYESTATE}_** | `EP_C_`     | Eye state during epoch: `C` = Eyes closed; `O` = Eyes open                |\n| **{EPOCH#}**       | `1`         | Epoch number (1 or 2) two epochs per eye state                            |\n| **can_**           | Fixed       | \"Channel\" prefix                                                          |\n| **{CHANNEL}**      | `A1`        | Electrode position (ABCD system): First letter = A • B • C • D <br>- Number = Electrode ID (1-32)                                                                                   |\n#### File Contents:\nEach Excel file contains 7 features for the specified band/channel/epoch combination:\n1. Mean Power\n2. RMS of PSD\n3. Standard Deviation\n4. Min Power\n5. Max Power\n6. Skewness\n7. Kurtosis\n#### Examples:\n1. `FR_Dats_band_ALFA_EP_C_1_can_A1.xlsx`\n   - Alpha band features\n   - First closed-eyes epoch\n   - Channel A1 (Frontal electrode 1)\n2. `FR_Dats_band_THETA_EP_O_2_can_C15.xlsx`\n   - Theta band features\n   - Second open-eyes epoch\n   - Channel C15 (Posterior electrode 15)\n3. `FR_Dats_band_BETA_EP_C_2_can_B7.xlsx`\n   - Beta band features\n   - Second closed-eyes epoch\n   - Channel B7 (Central electrode 7)\n#### Dataset Structure:\n- 4 epochs per subject:\n  - 2 closed-eyes: `EP_C_1`, `EP_C_2`\n  - 2 open-eyes: `EP_O_1`, `EP_O_2`\n- 128 channels (A1-D32)\n- 4 frequency bands\n- Total files per subject: 4 epochs × 128 channels × 4 bands = 2,048 files\n### EEG Data\n```\nEEG_JO_Dataset/\n├── code/\n├── sub-{Subject ID}{Group}/\n|   ├── eeg/\n|   |   ├── sub-{Subject ID}{Group}_coordsystem.json\n|   |   ├── sub-{Subject ID}{Group}_electrodes.tsv\n|   |   ├── sub-{Subject ID}{Group}_task-{Task Name}_acq-{Datatype}_eeg.json # Epoched data sidecar json\n|   |   ├── sub-{Subject ID}{Group}_task-{Task Name}_acq-{Datatype}_eeg.set # Epoched data\n|   |   ├── sub-{Subject ID}{Group}_task-{Task Name}_channels.tsv\n|   |   ├── sub-{Subject ID}{Group}_task-{Task Name}_desc-{Datatype}_eeg.json # Preprocessed data sidecar json\n|   |   └── sub-{Subject ID}{Group}_task-{Task Name}_desc-{Datatype}_eeg.set # Preprocessed data\n├── ...\n├── CHANGES\n├── dataset_description.json\n├── participants.json\n├── participants.tsv\n└── README.md\n```\n#### File Nomenclature\n| Denomination          | Value           | Description                                                      |\n|-----------------------|-----------------|------------------------------------------------------------------|\n| `sub-`                | Fixed      \t    | Subject prefix                                                   |\n| `{Subject ID}`        | Fixed           | **Unique identifier**:<br>- First digit = group (`1`=sg, `1`=sg2, `2`=cg) <br>- Last 3 digits = subject ID                                                                     |\n| `{Group}`             | `cg`/`sg`/`sg2` | **Group**: `cg`=control, `sg`=study group 1, `sg2`=study group 2 |\n| `{Task Name}`         | `restingstate`  | **Task name** (resting state)                                    |\n| `acq-` `desc-`        | `acq-`/`desc-`  | **Label**: `acq-` = acquisition, `desc-` = description           |\n| `{Datatype}`          | `epochs`/`preprocessed` | Adquisition type                                         |\n| `eeg`                 | Electroencephalography data | Data type                                            |\n| Extension             | `.set`          | **File type**: processed                                         |\n#### Examples\n1. `sub-1005sg_task-restingstate_acq-epochs_eeg.set` = Epochs EEG for **study group 1** subject 005 (full ID 1005)\n2. `sub-1005sg_task-restingstate_desc-preprocessing_eeg.set` = Preprocessed EEG for **study group 1** subject 005 (full ID 1005)\n## Methods\n### EEG Acquisition\n- **Device**: Biosemi ActiveTwo system\n- **Electrodes**: 128 channels (radial placement, 10-20 system reference)\n- **Additional channels**: EOG, ECG recorded\n- **Sampling rate**: 2048 Hz (downsampled to 128 Hz during preprocessing)\n- **Online filtering**: 0.1-100 Hz bandpass\n- **Setup**:\n  - Participants seated awake\n  - Continuous monitoring for movements/sleep\n  - Event markers via serial communication (paradigm triggers)\n### Paradigms\n*(Dataset contains only resting-state recordings)*\n- **Resting State (RS)**:\n  - Total duration: 12 minutes\n  - Sequence:\n    - 4 min alternating eyes closed/open (COCO: Closed-Open-Closed-Open)\n    - 8 min eyes closed (excluded from current dataset)\n- **Segment trimming**:\n    - 5s post-event onset\n    - 5s pre-event offset (to avoid transition artifacts)\n### Preprocessing pipeline (EEGLAB/MATLAB)\n1. **Visual inspection**:\n   - Raw data review using BDFreader\n   - Identification of bad channels/artifacts\n2. **Downsampling**:\n   - 2048 Hz → 128 Hz (resting-state data)\n3. **Rereferencing**:\n   - Average reference (replaced failed earlobe reference)\n4. **Filtering**:\n   - Bandpass FIR: 1-40 Hz\n   - High-pass: 1 Hz (0.5 Hz cutoff, 425 points)\n   - Low-pass: 40 Hz (45 Hz cutoff, 45 points)\n5. **Artifact Removal**:\n   - Bad channel rejection:\n     - Flat signals > 5s\n     - SD > 4\n     - Correlation < 0.8 with neighbors\n   - ASR (Artifact Subspace Reconstruction)\n   - ICA + ICLabel (components >90% non-brain removed)\n### Feature Extraction\n- **PSD Calculation**: Welch's method (50% overlap, Hamming window)\n- **Frequency bands**:\n  - Delta (δ): 1-4 Hz\n  - Theta (θ): 4-8 Hz\n  - Alpha (α): 8-13 Hz\n  - Beta (β): 13-30 Hz\n- **Features per band/channel**:\n  1. Mean Power\n  2. RMS of PSD\n  3. Standard Deviation\n  4. Minimum Power\n  5. Maximum Power\n  6. Skewness\n  7. Kurtosis\n- **Feature volume**: 14,336 features/subject (4 bands × 128 channels × 4 segments × 7 features)\n### Technical Specifications\n- **Processing Hardware**:\n  - Intel Core i5-9400F @2.9GHz\n  - 16GB RAM\n  - Windows 10 (64-bit)\n- **Software**:\n  - MATLAB 2020a\n  - EEGLAB toolbox\n  - Python (scikit-learn, pandas for feature selection)\n- **Processing Time**: ~10 minutes/subject\n## Funding\nThis 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.\n## Support\nCorrespondence: Aura Polo (apolol@unimagdalena.edu.co); Elmer León (elmerleondb@unimagdalena.edu.co); Julie Viloria-Porto (julieviloriapp@unimagdalena.edu.co)","recording_modality":["eeg"],"senior_author":"Julie Viloria-Porto","sessions":[],"size_bytes":8691122065,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["restingstate"],"timestamps":{"digested_at":"2026-04-22T12:29:51.590062+00:00","dataset_created_at":"2025-11-11T00:27:28.823Z","dataset_modified_at":"2025-11-11T04:35:58.000Z"},"total_files":280,"storage":{"backend":"s3","base":"s3://openneuro.org/ds006923","raw_key":"dataset_description.json","dep_keys":["CHANGES","README.md","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"28d114e1194d5e26","model":"openai/gpt-5.2","tagged_at":"2026-01-20T19:14:55.402266+00:00"},"tags":{"pathology":["Other"],"modality":["Resting State"],"type":["Clinical/Intervention"],"confidence":{"pathology":0.8,"modality":0.9,"type":0.7},"reasoning":{"few_shot_analysis":"Closest convention match is the few-shot example: \"A dataset of EEG recordings from: Alzheimer's disease, Frontotemporal dementia and Healthy subjects\" which is (1) a case/control cohort, (2) uses eyes-closed resting EEG, and (3) is labeled Modality=Resting State and Type=Clinical/Intervention. This guides the convention that when the dataset’s purpose is group comparison/biomarker-like characterization of a special/clinical population using resting EEG, the Type can be Clinical/Intervention rather than Resting-state. For a contrasting convention, the few-shot example \"A Resting-state EEG Dataset for Sleep Deprivation\" (healthy participants, resting EEG) is labeled Type=Resting-state, suggesting Type shifts toward Resting-state when the study is not primarily a special-population/clinical characterization.","metadata_analysis":"Key population facts: (1) \"resting-state EEG data collected ... of 140 participants: - 74 juvenile offenders (JO) - 66 juvenile non-offender controls\" and (2) the project goal is \"reconocimiento de patrones asociados a disfunciones neurocognitivas en jóvenes en conflicto con la ley\" (pattern recognition associated with neurocognitive dysfunction in youth in conflict with the law). Key task/paradigm facts: (3) \"(Dataset contains only resting-state recordings)\" and (4) \"Resting State (RS): ... 4 min alternating eyes closed/open (COCO: Closed-Open-Closed-Open)\".","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: Metadata SAYS the recruited special population is \"74 juvenile offenders (JO)\" with \"66 juvenile non-offender controls\" (not a named medical diagnosis). Few-shot pattern SUGGESTS using specific diagnoses when stated; otherwise special populations (e.g., visually deprived) map to Other. ALIGN (no conflict): choose Other.\nModality: Metadata SAYS \"only resting-state recordings\" with eyes closed/open (COCO). Few-shot pattern SUGGESTS resting EEG -> Modality=Resting State. ALIGN.\nType: Metadata SAYS resting-state paradigm, but also an applied/biomarker-like goal: \"reconocimiento de patrones asociados a disfunciones neurocognitivas\" in juvenile offenders. Few-shot pattern SUGGESTS that resting EEG with a primary special/clinical group-comparison goal can be Type=Clinical/Intervention (as in the dementia resting dataset), whereas normative resting datasets are Type=Resting-state. PARTIAL ALIGN/AMBIGUITY (no direct conflict): both Type labels plausible; select Clinical/Intervention because the dataset is organized around an offender vs control comparison and neurocognitive dysfunction recognition as the stated project aim.","decision_summary":"Top-2 candidates per category:\nPathology: (1) Other — supported by \"74 juvenile offenders (JO)\" and targeted recruitment from a re-education foundation; (2) Healthy — plausible because there are \"juvenile non-offender controls\" and no explicit psychiatric diagnosis. Head-to-head: dataset is defined by an offender cohort rather than a purely normative sample -> Other. Evidence alignment: aligns with few-shot convention for non-diagnostic special populations.\nModality: (1) Resting State — supported by \"only resting-state recordings\" and \"eyes closed/open (COCO)\"; (2) Unknown — if task were unclear, but it is explicit. Choose Resting State.\nType: (1) Clinical/Intervention — supported by project aim \"reconocimiento de patrones asociados a disfunciones neurocognitivas\" and case/control design (JO vs controls); consistent with the few-shot dementia resting dataset convention; (2) Resting-state — supported by purely resting paradigm description. Head-to-head: primary stated purpose is neurocognitive dysfunction pattern recognition in a special cohort -> Clinical/Intervention. Confidence lower due to overlap with Resting-state label."}},"computed_title":"Dataset of Electroencephalograms of Juvenile Offenders","nchans_counts":[{"val":128,"count":280}],"sfreq_counts":[{"val":128.0,"count":280}],"stats_computed_at":"2026-04-22T23:16:00.312179+00:00","total_duration_s":134400.0,"author_year":"Polo2025","canonical_name":null}}