{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a33ff","dataset_id":"ds005555","associated_paper_doi":null,"authors":["Eduardo López-Larraz","María Sierra-Torralba","Sergio Clemente","Galit Fierro","David Oriol","Javier Minguez","Luis Montesano","Jens G. Klinzing"],"bids_version":"1.8.0","contact_info":["Eduardo López-Larraz"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds005555.v1.1.1","datatypes":["eeg"],"demographics":{"subjects_count":128,"ages":[27,27,27,26,27,26,33,38,32,41,28,71,41,24,31,71,40,67,31,67,34,34,31,28,25,74,22,23,31,36,66,69,81,66,69,67,82,64,67,66,69,82,25,26,24,82,21,26,64,33,33,64,64,26,27,26,27,29,29,29,29,29,29,29,29,57,28,26,57,47,60,38,34,63,51,50,29,59,26,27,29,31,34,18,18,22,44,25,60,60,24,26,37,31,24,45,29,57,42,33,23,25,28,22,22,26,21,70,70,70,73,73,73,65,65,65,62,62,62,67,27,30,35,32,24,22,23],"age_min":18,"age_max":82,"age_mean":42.039370078740156,"species":null,"sex_distribution":{"f":76,"m":52},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds005555","osf_url":null,"github_url":null,"paper_url":null},"funding":["This work was funded by Programa Misiones de I+D en Inteligencia Artificial (AI4HEALTHYAGING: MIA.2021.M02.0007)","Penta call 5 – Joint call – Euripides (pAvIs: IDI-20210522)","H2020 FET-Open call (MITICS: 964677)","Horizon-EIC-2022 call (BAYFLEX: 101099555)","Horizon-RIA-2023 call (MANOLO: 101135782)","Programa Aragon Investigo (Exp: Z-0073-INVESCS-22)","Government of Aragon (IDMF/2023/0007)","Programa red.es (2021/C005/00143341)."],"ingestion_fingerprint":"fc49ff86fe97c527aed121e187ca9dc001fa544918af244fd34d6cd3c97802de","license":"CC0","n_contributing_labs":null,"name":"The Bitbrain Open Access Sleep (BOAS) dataset","readme":"# README\nThe **Bitbrain Open Access Sleep (BOAS)** dataset.\n## Overview\nThis project aimed at bridging the gap between gold-standard clinical sleep monitoring and emerging wearable EEG technologies. The dataset contains data from **128 nights** in which participants were simultaneously monitored with two technologies: a **Brain Quick Plus Evolution PSG system by Micromed** and a **wearable EEG headband by Bitbrain**. The Micromed PSG system records a comprehensive and clinically validated set of physiological sleep parameters, while the Bitbrain wearable EEG headband offers a user-friendly, self-administered alternative, limited to forehead EEG electrodes, movement sensors, and photo-plethysmography. **Data from both systems were acquired simultaneously**, allowing for direct comparison and validation of the wearable EEG device against the established PSG standard. This dual-recording approach provides a rich resource for evaluating the performance and potential of wearable EEG technology in sleep studies.\n**Human sleep scoring:** To ensure robust and reliable sleep staging, we followed a rigorous labeling process. **Three expert sleep scorers independently annotated the PSG recordings** following criteria developed by the American Academy of Sleep Medicine (AASM) (Berry et al., 2015). From the resulting three scorings, a **consensus label** was derived: each epoch of sleep data received the label scored by at least two of the scorers. In cases where all three scorers had given different labels, a fourth scorer made the final decision. This consensus labeling approach addresses the inherent variability in human-derived sleep scoring, with an estimated inter-scorer agreement of approximately 85% (Danker-Hopfe et al., 2009; Rosenberg and Van Hout, 2013).\n**Automatic scoring:** We used the human expert consensus labels to train a deep learning model (Esparza-Iaizzo et al., 2024). By implementing a cross-validation procedure, we trained and validated the model separately on the PSG and wearable EEG datasets. The model achieved an 87.13% match between human-consensus and network-provided labels for the PSG data, and an 86.71% match for the wearable EEG data.\nOur dataset includes:\n1. **PSG recordings** from 128 nights (files ending with \"*psg_eeg.edf*\"),\n2. **Wearable EEG recordings** from the same nights (files ending with \"*headband_eeg.edf*\"),\n3. **Human-consensus sleep stage labels**, obtained from the PSG recordings (\"*stage_hum*\" in the PSG data's event files),\n4. **AI-generated sleep stage labels**, separately obtained from PSG recordings and from wearable EEG recordings (\"*stage_ai*\" in both the PSG and headband data's event files).\n5. **Further meta data** for each recording (i.e., the participants' age, sex, and BMI, provided in the file \"*participants.tsv*\")\n## Participants\nParticipants were members of the general population, provided written informed consent, and received economic compensation of 50€ per night.\nIn order to represent the general population, we recruited a broad spectrum of participants along the dimensions of age, sex, and body mass index. We did not recruit patients with particular health conditions but only excluded severe conditions that could have affected the feasibility or safety of the study. In detail, inclusion and exclusion criteria were as follows.\n**Inclusion criteria**\n- Age > 18 years,\n- Sufficient knowledge of Spanish to understand the explanatory text, the consent form and study-related instructions.\n**Exclusion criteria**\n- Current severe medical interventions or medication,\n- History of severe neurological or psychiatric disorders,\n- Severe health problems in the last 12 months (especially neurological or cardiac disorders),\n- Current pregnancy or nursing,\n- Use of psychotropic medication, benzodiazepines, gamma-hydroxybutyric acid, and similar drugs before or during the study.\n## Format\nThe dataset is formatted according to the Brain Imaging Data Structure (BIDS). Please note that while the recordings are named from sub-1 up to sub-128, some come from the same participants. 108 unique individuals participated in the recordings, data of which can be matched using the pid (= unique participant ID) property in the file \"*participants.tsv*\"\nThe folder of each recording contains the data recorded with the PSG (\"*sub-xx_task-Sleep_acq-psg_eeg.edf*\") and with the wearable EEG headband (\"*sub-xx_task-Sleep_acq-headband_eeg.edf*\").\n**Channel groups**\nNot all recordings contain data from all available sensors. The full list of available sensors for each recording can be obtained on the \"*channels.tsv*\" file. Channels in this file are coded in groups:\n- **PSG_EEG**: Electroencephalography recorded with the PSG system. Channels available are F3, F4, C3, C4, O1, O2 (PSG_F3, PSG_F4, PSG_C3, PSG_C4, PSG_O1, PSG_O2).\n- **PSG_EOG**: Electrooculography signals recorded with the PSG system. The location of the EOG electrodes was lateral of the eyes; one slightly lower than the participant's left eye and one slightly higher than the participant's right eye (according to AASM guidelines). For recordings containing only one EOG channel (PSG_EOG), the electrodes were recorded as a bipolar derivation. If two EOG channels are present (PSG_EOGR, PSG_EOGL), both electrodes were referenced against the left mastoid.\n- **PSG_EMG**: Electromyography signals recorded with the PSG system. Data contain a single EMG channel (PSG_EMG), which is the result of a bipolar derivation of two chin electrodes.\n- **PSG_BELTS**: Breathing activity recorded by the PSG system using abdominal and thoracic breathing belts (PSG_ABD, PSG_THOR).\n- **PSG_THER**: Respiratory airflow recorded with the PSG system using a thermistor (PSG_THER).\n- **PSG_CAN**: Respiratory airflow recorded with the PSG system using a nasal cannula (PSG_CAN).\n- **PSG_PPG**: Photopletismographic (PPG) activity recorded with the PSG system. Channels available are pulse (PSG_PULSE), heart beat (PSG_BEAT) and oxygen saturation (PSG_SPO2).\n- **HB_EEG**: Electroencephalography recorded with the wearable EEG headband. Headband channels are approximately located at AF7 and AF8 (HB_1, HB_2).\n- **HB_IMU**: Movement activity recorded by an Inertial Measurement Unit (IMU) in the headband. Signals are derived from an accelerometer (HB_IMU_1, HB_IMU_2, HB_IMU_3) and gyroscope (HB_IMU_4, HB_IMU_5, HB_IMU_6), both recording signals for all three spatial dimensions.\n- **HB_PULSE**: Pulse activity recorded with the wearable EEG headband using a PPG sensor (HB_PULSE).\n**Sleep staging labels**\nThe sleep stage labels for each recording are coded as events in corresponding event files (stage_hum and stage_ai; see above). Stages are coded as follows:\n- 0: Wake,\n- 1: NonREM sleep stage 1 (N1),\n- 2: NonREM sleep stage 2 (N2),\n- 3: NonREM sleep stage 3 (N3),\n- 4: REM sleep,\n- 8: PSG disconnections (e.g., due to bathroom breaks; human-scored only)\n- -2: Artifacts and missing data (AI-scored only)\n## References\nBerry, R. B., Brooks, R., Gamaldo, C. E., Harding, S. M., Lloyd, R. M., Marcus, C. L., et al. (2015). The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications, Version 2.2. Darien, Illinois.\nDanker-Hopfe, H., Anderer, P., Zeitlhofer, J., Boeck, M., Dorn, H., Gruber, G., et al. (2009). Interrater reliability for sleep scoring according to the Rechtschaffen & Kales and the new AASM standard. J. Sleep Res. 18, 74–84. doi: 10.1111/j.1365-2869.2008.00700.x.\nEsparza-Iaizzo, M., Sierra-Torralba, M., Klinzing, J. G., Minguez, J., Montesano, L., and López-Larraz, E. (2024). Automatic sleep scoring for real-time monitoring and stimulation in individuals with and without sleep apnea. bioRxiv, 2024.06.12.597764. doi: 10.1101/2024.06.12.597764.\nRosenberg, R. S., and Van Hout, S. (2013). The American Academy of Sleep Medicine inter-scorer reliability program: sleep stage scoring. J. Clin. sleep Med. 9, 81–87. doi: 10.5664/jcsm.2350.\n## Contact\nIf you have any questions or comments, please contact:\nEduardo López-Larraz: eduardo.lopez@bitbrain.com\nJens G. Klinzing: jens.klinzing@bitbrain.com","recording_modality":["eeg"],"senior_author":"Jens G. Klinzing","sessions":[],"size_bytes":35921406226,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["Sleep"],"timestamps":{"digested_at":"2026-04-22T12:28:36.271009+00:00","dataset_created_at":"2024-10-03T09:33:12.891Z","dataset_modified_at":"2025-05-22T12:37:27.000Z"},"total_files":256,"storage":{"backend":"s3","base":"s3://openneuro.org/ds005555","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","channels.json","channels.tsv","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"639ac326bd4512a9","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Healthy"],"modality":["Sleep"],"type":["Sleep"],"confidence":{"pathology":0.9,"modality":0.95,"type":0.95},"reasoning":{"few_shot_analysis":"Closest few-shot match is the \"Surrey cEEGrid sleep data set\" example (Healthy / Sleep / Sleep): it is an overnight sleep dataset with no active task and sleep staging focus. BOAS similarly records overnight sleep and provides sleep stage labels (human consensus and AI). This guides mapping to Modality=Sleep and Type=Sleep, and Pathology=Healthy when recruitment is from general population without disorder targeting. The epilepsy HFO example shows that even when sleep is recorded, if the recruited cohort is clinical (pediatric epilepsy), Pathology becomes Epilepsy; BOAS explicitly does not recruit a clinical cohort, so it follows the Healthy sleep convention like Surrey.","metadata_analysis":"Key population facts: \"Participants were members of the general population\" and \"We did not recruit patients with particular health conditions\" and exclusion criteria include \"History of severe neurological or psychiatric disorders\".\nKey paradigm/stimulus facts (sleep): \"dataset contains data from 128 nights\" with \"gold-standard clinical sleep monitoring\" and files named \"sub-xx_task-Sleep_acq-psg_eeg.edf\" / \"sub-xx_task-Sleep_acq-headband_eeg.edf\".\nKey research purpose (sleep staging): \"Three expert sleep scorers independently annotated the PSG recordings\" and \"a consensus label was derived\" and labels include \"Human-consensus sleep stage labels\" and \"AI-generated sleep stage labels\" with stage codes \"0: Wake... 4: REM sleep\".","paper_abstract_analysis":"No useful paper information (only references are listed; no abstract content provided in the dataset metadata).","evidence_alignment_check":"Pathology:\n- Metadata says: \"Participants were members of the general population\" and \"We did not recruit patients with particular health conditions\" (plus exclusions for severe disorders). \n- Few-shot pattern suggests: general-population sleep datasets map to Healthy (e.g., Surrey sleep).\n- Alignment: ALIGN.\n\nModality:\n- Metadata says: \"128 nights\" sleep monitoring, \"task-Sleep\" filenames, and explicit \"sleep stage labels\" (Wake/N1/N2/N3/REM).\n- Few-shot pattern suggests: overnight sleep datasets map to Modality=Sleep (Surrey sleep).\n- Alignment: ALIGN.\n\nType:\n- Metadata says: focus on \"sleep scoring\", \"sleep staging\", \"consensus label\", and training a model for \"Automatic scoring\" of sleep stages.\n- Few-shot pattern suggests: sleep staging/monitoring datasets map to Type=Sleep (Surrey sleep).\n- Alignment: ALIGN.","decision_summary":"Top-2 candidates and final selections:\n\n1) Pathology\n- Candidate A: Healthy (SELECTED)\n  Evidence: \"Participants were members of the general population\"; \"We did not recruit patients with particular health conditions\"; exclusion includes \"History of severe neurological or psychiatric disorders\".\n  Few-shot alignment: matches Surrey sleep example labeled Healthy.\n- Candidate B: Unknown\n  Evidence-for: could include undiagnosed sleep issues in general population; referenced work mentions \"with and without sleep apnea\" but recruitment was not by diagnosis.\nHead-to-head: Metadata explicitly describes non-clinical recruitment, so Healthy wins.\n\n2) Modality\n- Candidate A: Sleep (SELECTED)\n  Evidence: \"128 nights\"; \"task-Sleep\" file naming; explicit staging labels \"Wake, N1, N2, N3, REM\".\n  Few-shot alignment: Surrey sleep example uses Sleep modality.\n- Candidate B: Resting State\n  Evidence-for: sleep is sometimes confused with resting/eyes-closed, but here it is full-night sleep with staging.\nHead-to-head: Explicit sleep-night and staging language makes Sleep clearly stronger.\n\n3) Type\n- Candidate A: Sleep (SELECTED)\n  Evidence: \"Human sleep scoring\"; \"consensus label\"; \"Automatic scoring\"; stage label taxonomy.\n- Candidate B: Clinical/Intervention\n  Evidence-for: comparison to PSG and wearable validation could be seen as applied/clinical-tech validation, but not an intervention nor a clinical cohort study.\nHead-to-head: Primary construct is sleep and sleep staging, so Type=Sleep.\n\nConfidence justification:\n- Pathology: multiple explicit recruitment statements (general population; no targeted conditions; exclusion of severe disorders) + strong few-shot analog => high.\n- Modality: multiple explicit sleep-night and staging quotes + strong few-shot analog => very high.\n- Type: multiple explicit sleep scoring/staging/automatic scoring quotes + strong few-shot analog => very high."}},"nemar_citation_count":0,"computed_title":"The Bitbrain Open Access Sleep (BOAS) dataset","nchans_counts":[{"val":9,"count":96},{"val":11,"count":68},{"val":14,"count":25},{"val":16,"count":19},{"val":3,"count":18},{"val":2,"count":16},{"val":8,"count":9},{"val":15,"count":5}],"sfreq_counts":[{"val":256.0,"count":256}],"stats_computed_at":"2026-04-22T23:16:00.310575+00:00","total_duration_s":7209330.0,"canonical_name":null,"name_confidence":0.7,"name_meta":{"suggested_at":"2026-04-14T10:18:35.343Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"canonical","author_year":"LopezLarraz2024"}}