{"success":true,"database":"eegdash","data":{"_id":"696ffc370e08707cf2f2a02a","dataset_id":"ds007262","associated_paper_doi":"10.1049/htl.2015.0037","authors":["Matthew Barras","Liam Booth"],"bids_version":"1.9.0","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds007262.v1.0.6","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":{"paper_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/4814836"},"funding":[],"ingestion_fingerprint":"a6e7b7d2fc730b1af7b20b92d09c80318e082391d19c387a324949008ea3caaa","license":"CC0","n_contributing_labs":null,"name":"Cognitive Workload 8-level arithmetic","readme":"This dataset was generated from LSL/XDF recordings. Converted to bids with instructions and code [presented here](https://github.com/LMBooth/QT-arithmetic_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 (arithmetic-Markers) and acquisition dropout annotations (UoHDataOffsetStream); events include a marker_stream column and marker definitions are in task-arithmetic_events.json.\n- Pupil Labs gaze/pupil data was exported from the XDF pupil_capture stream into sub-*/eeg/ as eyetrack physio files (*_recording-eyetrack_physio.tsv.gz + *_recording-eyetrack_physio.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.gz + *_recording-ecg_physio.json.\n- ML analysis note: participants excluded from the ML 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 multi-modal acquisition failures (sub-002, 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- Arithmetic task difficulty was defined using Q-value ranges and randomized order across trials.\n- Task events encode difficulty in `trial_type` and `difficulty_range` (e.g.,baseline, 0.6-1.5, 1.5-2.4, ..., 6.0-6.9).\n- Baseline for 60 seconds and then 70 questions, 10 at each difficulty level presented for 6 seconds each.\nTask: arithmetic\nRelease notes\n- Recorded 20 participants; released 18.\n- Reason: multi-modal acquisition QC failure.\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., Höchenberger, 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":null,"sessions":[],"size_bytes":2062514356,"source":"openneuro","storage":{"backend":"s3","base":"s3://openneuro.org/ds007262","raw_key":"dataset_description.json","dep_keys":["CHANGES","README.md","datacite.yml","participants.json","participants.tsv","task-arithmetic_eeg.json","task-arithmetic_events.json"]},"study_design":null,"study_domain":null,"tasks":["arithmetic"],"timestamps":{"digested_at":"2026-05-31T16:28:42.206066+00:00","dataset_created_at":null,"dataset_modified_at":null},"total_files":18,"computed_title":"Cognitive Workload 8-level arithmetic","nchans_counts":[{"val":24,"count":18}],"sfreq_counts":[{"val":250.0,"count":18}],"stats_computed_at":"2026-05-31T19:34:32.603515+00:00","total_duration_s":16500.384,"tagger_meta":{"model":"openai/gpt-4o","tagged_at":"2026-06-10T08:19:41Z","source":"eegdash-llm-tagger"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Decision-making"],"confidence":{"pathology":0.8,"modality":0.8,"type":0.8},"reasoning":{"few_shot_analysis":"The 'Cognitive Workload 8-level arithmetic' dataset involves an arithmetic task that manipulates difficulty levels to study cognitive workload. A similar few-shot example is the 'EEG, pupillometry, ECG and photoplethysmography, and behavioral data in the digit span task and rest' dataset, where working memory tasks involved auditory modality with a focus on Memory. The task type of 'working memory' in the few-shot example corresponds to 'Memory.' Therefore, calculating cognitive workload through an arithmetic task is likely aligned with a Decision-making label due to the assessment of cognitive load management and decision efficiency under different difficulty conditions.","metadata_analysis":"The metadata specifies, 'Task: arithmetic' and details an arithmetic task with difficulty levels (e.g., '0.6-1.5', '1.5-2.4'), indicating a task structure designed to measure cognitive workload and performance. It also emphasizes 'Arithmetic task difficulty' and varied 'trial_type' and 'difficulty_range,' suggesting performance under cognitive load ('Cognitive Workload 8-level arithmetic').","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: The metadata makes no mention of a specific clinical condition or target group, indicating a 'Healthy' participant cohort aligning well with general cognitive workload measurements.\nModality: Task metadata highlights arithmetic tasks typically requiring visual input (e.g., visual presentation of arithmetic problems), thus aligning with 'Visual.'\nType: Few-shot examples with cognitive workload tasks like digit span tests underlined Memory but this metadata uses an arithmetic approach linked more closely to efficiency in problem-solving, aligning with 'Decision-making.'","decision_summary":"Pathology: Top-2 candidates were 'Healthy' (as default with no clinical focus) and 'Unknown.' Since participants appear healthy ('age', 'handedness'), 'Healthy' is selected. Modality: 'Visual' (arithmetic tasks usually need visual representation) vs. 'Unknown,' with 'Visual' being stronger. Type: The labels 'Decision-making' (arithmetic under cognitive load conditions), and 'Memory,' but 'Decision-making' stronger due to the specific load management aspect discussed ('Arithmetic task difficulty')."}},"canonical_name":null,"name_confidence":0.87,"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_Cognitive","bad_channels_info":null,"generated_by":[{"Name":"MNE-BIDS","Version":"0.18.0","CodeURL":"https://mne.tools/mne-bids/"}],"source_datasets":[{"Directory":"sourcedata","Version":"1.0.0"}],"associated_paper_meta":{"channel":"text/readme","confidence":"medium","author_overlap":0,"is_oa":true,"oa_status":"green","source":"paper_resolver"}}}