{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a33c0","dataset_id":"ds005185","associated_paper_doi":null,"authors":["Kaare B. Mikkelsen","Preben Kidmose","Yousef Rezaei Tabar"],"bids_version":"1.7.0","contact_info":["kaare mikkelsen"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds005185.v1.0.2","datatypes":["eeg"],"demographics":{"subjects_count":20,"ages":[23,23,24,25,24,26,25,27,36,34,27,30,29,23,28,22,23,24,22,23],"age_min":22,"age_max":36,"age_mean":25.9,"species":null,"sex_distribution":{"f":13,"m":7},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds005185","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"2351822c64a7213f9384822fa91ece72ccf5f11fe84eec07afdc5ed991d6a99c","license":"CC0","n_contributing_labs":null,"name":"Ear-EEG Sleep Monitoring 2019 (EESM19)","readme":"EESM19: Ear-EEG Sleep Monitoring data set\nThis data set was collected as part of development and quality assessment of the ear-EEG as a sleep monitoring platform. Data collection took place between 2018 and 2020. First publication was in 2019 (https://doi.org/10.1038/s41598-019-53115-3), hence the '19' in the name.\nThe data set consists of 2 parts (a & b):\na: 20 subjects who each spent 4 nights sleeping with a partial PSG (EEG, EOG and chin EMG electrodes), ear-EEG and a wristworn actigraph, in their own homes.\nb: Of these 20 subjects, 10 also slept a further 12 nights wearing only ear-EEG, actigraph and a single EOG electrode.\nEach night is saved as a separate ‘session’, meaning that some subjects have 4 sessions while others have 16. The PSG-nights area always sessions 1-4. Each PSG night has an additional 'scoring' event file, where 'scoring' is the 'acquisition' type.\nQuestionnaires:\nAfter each night’s recording, the subject answered a short questionnaire regarding the quality of the night’s sleep. This has been archived as behavioral data (task='comfort').\nDiaries:\nBesides the comfort questionnaire, the subjects also kept a standardized diary regarding the events of the night. This have been imported too, however only the requried fields 'Syncronization','Electrodetest','Went to bed', 'Lights out' and 'Got up' have been translated from Danish to English. We suggest using an online translation tool for any additional entries.\nThe diaries have a column 'pressedTrigger', which indicates that the subject marked the precise time of the event on their wrist worn actigraph. As there is some interpretation necessary due to both spurious extra trigger presses and also missing trigger presses, and these event markings eventually turned out not to be important for our own research, we have not exported these trigger times in the data set. However, as the full actigraphy file is included in this data set, any interested future user can do the matching themselves.\nFor consistency, we have chosen to use the starting time written in the scored edf file ('edf1') as the starting time of each PSG recording. For non-PSG recordings, the starting time is what is written in the diary. An alternative would be using the start time as seen in the wrist actigraph, described below.\nActigraphy:\nSubjects wore GENEactive actigraphs ('actiwatches' for short). These record 3-axis acceleration as well as temperature, light and user button presses. Given that the temperature and light readings are very impacted by whether the subjects had their hand above or below the covers, we found that only the actigraphy and button presses had much use. However, all data is found in the actigraphy files (in the behavior folders).\nThe ensure the possibility of perfect alignment between actiwatch and EEG recorder (TMSI 'mobita'), at the beginning of each recording, the subjects shook the mobita and the actiwatch together in a repeated rythmical pattern. By accessing the mobita actigraphy data from the .set file (EEG.etc.acc.data) it is possible to get perfect alignment. This is advantageous if very high precision of various sleep events is desired, since the clock in in the actiwatch was very reliable. In practice, we have not used this option, and hence the actigraphy alignment is left up to the user.\nElectrode test:\nAs a quality check on the electrode connections subjects viewed a short video containing various instructions: repeated jaw clenching, open/closed eyes, horizontal eye movements. These are marked in the diaries, and can be used as a simple test that the EEG equipment is working as intended. An analysis of these responses can be found in https://doi.org/10.3389/fncom.2021.565244.\nNote regarding artifact rejection:\nWe advice against using the data directly from the .poly5 files. The primary reason for this is that we had some issues with faulty shielding on some of the electrodes (good shielding is necessary for dry-contact electrodes). This caused signal leakage between electrodes, which is highly unwanted, and which could make the ear-EEG channels contain PSG data, even after rereferencing. We went to great lengths to identify these electrodes, using both algorithms and physical inspection of all electrodes between recordings, and are confident that there are no issues in the .set files (for which these electrodes have been set to 'NaN'). Note that that this identification and discarding is the only preprocessing which has been done to the EEG data.\nFor questions regarding this data set, contact:\nKaare Mikkelsen, Mikkelsen.kaare@ece.au.dk, https://orcid.org/0000-0002-7360-8629","recording_modality":["eeg"],"senior_author":"Yousef Rezaei Tabar","sessions":["001","002","003","004","005","006","007","008","009","010","011","012","013","014","015","016"],"size_bytes":287335439821,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["ASSR","electrodeTestLab","sleep"],"timestamps":{"digested_at":"2026-04-22T12:27:25.607739+00:00","dataset_created_at":"2024-05-25T21:50:05.988Z","dataset_modified_at":"2024-11-09T13:45:24.000Z"},"total_files":356,"storage":{"backend":"s3","base":"s3://openneuro.org/ds005185","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","acq-PSG_channels.tsv","acq-earEEG_channels.tsv","participants.json","participants.tsv","sessions.json","task-ASSR_eeg.json","task-comfort_beh.json","task-electrodeTestLab_eeg.json","task-sleep_acq-acti_physio.json","task-sleep_acq-diary_events.json","task-sleep_acq-scoring_events.json","task-sleep_events.json"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"64265b263904799b","model":"openai/gpt-5.2","tagged_at":"2026-01-20T17:42:17.918291+00:00"},"tags":{"pathology":["Healthy"],"modality":["Sleep"],"type":["Sleep"],"confidence":{"pathology":0.7,"modality":0.9,"type":0.9},"reasoning":{"few_shot_analysis":"Closest few-shot match is the \"Surrey cEEGrid sleep data set\" example (Healthy / Sleep / Sleep): it is also an at-home/night sleep monitoring dataset with no active task, focused on sleep recordings (PSG/around-the-ear EEG). That example guides the convention that such datasets are labeled Modality=Sleep and Type=Sleep, and Pathology=Healthy when no disorder-based recruitment is stated.","metadata_analysis":"Key facts from the dataset README indicate an overnight sleep monitoring study with ear-EEG/PSG and no clinical recruitment. Quotes: (1) \"Ear-EEG Sleep Monitoring data set\" and \"development and quality assessment of the ear-EEG as a sleep monitoring platform.\" (2) \"20 subjects who each spent 4 nights sleeping with a partial PSG ... ear-EEG and a wristworn actigraph, in their own homes.\" (3) \"10 also slept a further 12 nights wearing only ear-EEG\". There is no mention of patients or any diagnosis; instead the focus is sleep monitoring/staging infrastructure (PSG, EOG, chin EMG, actigraphy, night-by-night sessions).","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: Metadata SAYS no disorder recruitment (e.g., \"20 subjects\" for platform assessment; no diagnoses mentioned). Few-shot pattern SUGGESTS Healthy for sleep monitoring cohorts without a clinical condition (as in the Surrey cEEGrid sleep example). ALIGN.\nModality: Metadata SAYS sleep recordings (\"4 nights sleeping\", \"12 nights wearing only ear-EEG\", \"Sleep Monitoring\"). Few-shot pattern SUGGESTS Sleep for overnight sleep datasets. ALIGN.\nType: Metadata SAYS the purpose is sleep monitoring (\"sleep monitoring platform\"; nights of sleep + scoring files). Few-shot pattern SUGGESTS Type=Sleep (not Resting-state) when the dataset is explicitly sleep/PSG night recordings. ALIGN.","decision_summary":"Top-2 candidates per category:\n\nPathology:\n- Healthy (winner): supported by lack of diagnosis language and general-participant framing: \"20 subjects\"; collected for \"development and quality assessment\" rather than a disorder cohort.\n- Unknown (runner-up): could be considered if recruitment details were missing, but the README context strongly implies a normative cohort.\nAlignment: aligns with few-shot sleep-monitoring example.\n\nModality:\n- Sleep (winner): explicit overnight sleep: \"spent 4 nights sleeping\"; \"slept a further 12 nights\"; PSG sleep scoring.\n- Resting State (runner-up): plausible for eyes-closed resting protocols, but this dataset is nocturnal sleep with staging/scoring.\nAlignment: aligns with few-shot.\n\nType:\n- Sleep (winner): primary construct is sleep monitoring/staging infrastructure: \"sleep monitoring platform\" and nightly recordings with scoring files.\n- Resting-state (runner-up): not supported because recordings are during sleep, not waking rest.\nAlignment: aligns with few-shot.\n\nConfidence notes (quotes/features): Pathology confidence limited because README never explicitly says \"healthy\", but strongly implies non-clinical recruitment; Modality/Type have multiple explicit sleep quotes."}},"computed_title":"Ear-EEG Sleep Monitoring 2019 (EESM19)","nchans_counts":[{"val":25,"count":156}],"sfreq_counts":[{"val":500.0,"count":156}],"stats_computed_at":"2026-04-22T23:16:00.309083+00:00","total_duration_s":null,"canonical_name":null,"name_confidence":0.96,"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":"Mikkelsen2024_Ear_Sleep_Monitoring"}}