{"success":true,"database":"eegdash","data":{"_id":"69a33a3b897a7725c66f3ee8","dataset_id":"ds007169","associated_paper_doi":null,"authors":["Matthew Barras","Liam Booth"],"bids_version":"1.9.0","contact_info":["Liam Booth"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds007169.v1.0.5","datatypes":["eeg"],"demographics":{"subjects_count":18,"ages":[26,28,31,18,22,48,25,23,23,27,21,28,22,31,21,29,20,24,30,31],"age_min":18,"age_max":48,"age_mean":26.4,"species":null,"sex_distribution":{"f":6,"m":14},"handedness_distribution":{"r":17,"l":3}},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds007169","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"b2521a94b0659112deb957347e60069f1053a69d12eddda0f7ed6282f1cd7364","license":"CC0","n_contributing_labs":null,"name":"Multimodal Cognitive Workload n-back Task, 4 Difficulties","readme":"This dataset was generated from LSL/XDF recordings. Converted to BIDS with instructions and code [presented here](https://github.com/LMBooth/QT-nback_study/tree/main/conversion_package)\n- Original recordings are stored under sourcedata/xdf/ as .xdf files (non-BIDS).\n- EEG was converted to BrainVision format (.vhdr/.eeg/.vmrk) under each sub-*/eeg/.\n- *_events.tsv was generated from marker streams and then aligned so onset is relative to the EEG start time.\n- Marker streams include task markers (n-backMarkers) and acquisition dropout annotations (UoHDataOffsetStream); events include a marker_stream column and marker definitions are in task-nback_events.json.\n- Pupil Labs gaze/pupil data was exported from the XDF pupil_capture stream into sub-*/pupil as *_task-nback_pupil.tsv + *_task-nback_eyetrack.json (PhysioType=eyetrack).\n- ECG is captured on the EEG system; the ECG channel is typed in *_channels.tsv and exported as *_recording-ecg_physio.tsv + *_recording-ecg_physio.json under sub-*/ecg.\n- Analysis note: participants excluded from the analysis remain in participants.tsv with analysis_included=false; no epoch rejection was applied to this raw dataset.\n- Participant IDs match the original XDF filenames; missing IDs correspond to excluded participants.\nParticipants\n- N_recorded: 20\n- N_released: 18\n- Exclusions: 2 participants excluded due to data quality failures (sub-013, sub-017).\n- Demographics in participants.tsv: age (years), sex, handedness.\n- Excluded IDs remain in participants.tsv with analysis_included=false.\nHardware and data collection\n- Combined EEG+ECG mobile EEG system (Bateson and Asghar, 2021; Clewett et al., 2016) and Pupil Labs Pupil Core, synchronized via Lab Streaming Layer (LSL).\n- EEG: 19-channel 10-20 montage (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, O2), Ag/AgCl electrodes with linked-ear reference, 250 Hz; impedances checked and Neurgel EEG gel applied.\n- ECG: 3-lead on the same system; positive lead right shoulder/clavicle, negative lead left shoulder/clavicle, feedback lead lower left torso.\n- Pupillometry: Pupil Labs Pupil Core eye tracking with infrared illuminators; LSL relay with asynchronous sampling (timestamps per sample).\nProtocol summary\n- Tutorial phase with feedback: 20 trials at each level (1-back through 4-back) after a 60 s fixation.\n- Main experiment: 100 trials at each level (1-back through 4-back) with no feedback.\n- Each level begins with a 6.0 s instruction screen (\"Remember N steps back\").\n- Each trial shows a letter for 1.0 s, followed by a 0.7 s blank interval.\n- Task events encode nback_level, key_press, matched, response_accuracy, and tutorial flags in task-nback_events.json.\nTask: nback\nRelease notes\n- Recorded 20 participants; released 18.\n- Reason: data quality failures.\n- Participant IDs match original XDF filenames; missing IDs indicate excluded participants.\nReferences\n----------\nAppelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Hochenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896).https://doi.org/10.21105/joss.01896\nPernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103.https://doi.org/10.1038/s41597-019-0104-8\nClewett CJ, Langley P, Bateson AD et al (2016) Non-invasive, home-based electroencephalography hypoglycaemia warning system for personal monitoring using skin surface electrodes: a single-case feasibility study. Healthc Technol Lett 3:2-5. https://doi.org/10.1049/htl.2015.0037\nBateson AD, Asghar AUR (2021) Development and evaluation of a smartphone-based electroencephalography (EEG) system. IEEE Access. https://doi.org/10.1109/ACCESS.2021.3079992","recording_modality":["eeg"],"senior_author":"Liam Booth","sessions":[],"size_bytes":442139020,"source":"openneuro","storage":{"backend":"s3","base":"s3://openneuro.org/ds007169","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","datacite.yml","participants.json","participants.tsv","task-nback_eeg.json","task-nback_events.json"]},"study_design":null,"study_domain":null,"tasks":["nback"],"timestamps":{"digested_at":"2026-04-22T12:30:05.458957+00:00","dataset_created_at":"2026-01-04T23:52:09.952Z","dataset_modified_at":"2026-01-28T22:58:41.000Z"},"total_files":18,"computed_title":"Multimodal Cognitive Workload n-back Task, 4 Difficulties","nchans_counts":[{"val":24,"count":18}],"sfreq_counts":[{"val":250.0,"count":18}],"stats_computed_at":"2026-04-22T23:16:00.312566+00:00","total_duration_s":18326.28,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"4eee784b4d9a7a11","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Memory"],"confidence":{"pathology":0.7,"modality":0.8,"type":0.8},"reasoning":{"few_shot_analysis":"Most similar few-shot convention is the digit-span dataset (Healthy / Auditory / Memory), which treats classic working-memory load manipulations (varying list length) as Type=Memory, even though the mechanics include responses and cognitive load. By analogy, an n-back paradigm with multiple difficulty levels (1–4 back) is also a working-memory load manipulation → Type=Memory. For Modality, the motor imagery example shows that response actions are not the stimulus modality; similarly, here the stimulus is letters on a screen, so Modality should follow the visual presentation rather than keypresses or physiology streams.","metadata_analysis":"Key stimulus/task facts:\n- \"Each trial shows a letter for 1.0 s, followed by a 0.7 s blank interval.\" (visual letter presentation)\n- \"Each level begins with a 6.0 s instruction screen (\\\"Remember N steps back\\\").\" (screen-based instructions; working-memory requirement)\n- \"Main experiment: 100 trials at each level (1-back through 4-back) with no feedback.\" (n-back difficulty manipulation)\nPopulation facts:\n- \"Participants - N_recorded: 20 - N_released: 18\" and demographics \"Age range: 18-48\" with sex/handedness listed, but no diagnosis or patient recruitment described.\nModality vs sensors clarification:\n- Although titled \"Multimodal\" and includes \"Pupil Labs gaze/pupil data\" and \"ECG\", these are recording modalities; the described task stimuli are screen-presented letters and instruction screens.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: no clinical diagnosis/recruitment is mentioned; only general participant counts and demographics (e.g., \"N_released: 18\"; \"Age range: 18-48\").\n- Few-shot pattern suggests: datasets without explicit patient populations are labeled Healthy.\n- Alignment: ALIGN (implicit healthy/normative cohort; no contrary clinical facts).\n\nModality:\n- Metadata says: \"Each trial shows a letter\" and \"instruction screen\" (screen-based stimuli).\n- Few-shot pattern suggests: modality is defined by stimulus channel (e.g., digit-span uses Auditory because digits are presented auditorily); response type does not set modality.\n- Alignment: ALIGN → Visual.\n\nType:\n- Metadata says: \"n-back Task, 4 Difficulties\" and \"Remember N steps back\" indicating working-memory updating/manipulation under varying load.\n- Few-shot pattern suggests: classic working-memory load paradigms (digit span) map to Type=Memory.\n- Alignment: ALIGN → Memory.","decision_summary":"Top-2 candidates (with head-to-head selection):\n\nPathology:\n1) Healthy (selected): No clinical group described; participants listed only by demographics and exclusions (\"N_released: 18\"; \"Demographics in participants.tsv: age (years), sex, handedness\").\n2) Unknown: because the metadata does not explicitly state \"healthy\" or \"controls\".\nDecision: Healthy is stronger because the dataset describes a standard cognitive-task study with no disease recruitment details.\nConfidence basis: implicit-only evidence (no explicit 'healthy' phrase) → moderate.\n\nModality:\n1) Visual (selected): \"Each trial shows a letter\"; \"instruction screen\" indicates visual presentation.\n2) Multisensory/Other: title says \"Multimodal\" and includes pupil/ECG, but those are recordings, not stimuli.\nDecision: Visual wins because the only described stimuli are screen-based letters/instructions.\nConfidence basis: 2 explicit stimulus quotes.\n\nType:\n1) Memory (selected): n-back levels (\"1-back through 4-back\") and instruction \"Remember N steps back\" are canonical working-memory load manipulation.\n2) Attention: could be framed as sustained attention/vigilance under workload.\nDecision: Memory is primary because n-back is fundamentally a working-memory updating task with parametrically varied memory load.\nConfidence basis: 2 explicit quotes about n-back levels and remembering N-back."}},"canonical_name":null,"name_confidence":0.55,"name_meta":{"suggested_at":"2026-04-14T10:18:35.343Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"author_year","author_year":"Barras2026_Multimodal"}}